人工智慧在製藥領域的應用：按組件、技術、治療領域、應用、部署類型和最終用戶分類－2025-2032年全球預測

預計到 2032 年，人工智慧 (AI) 在製藥領域的市場規模將達到 1,111.3 億美元，複合年成長率為 27.61%。

策略實施：概述人工智慧如何重塑藥物研發、臨床營運、監管路徑、生產流程和商業策略。

人工智慧不再只是製藥營運中的實驗性輔助工具；它已發展成為一項至關重要的戰略能力，影響著藥物發現科學、臨床開發、監管策略、生產營運和商業性決策。本文將人工智慧定位為一種系統級力量，而非僅僅是一系列技術的集合，它正在重塑藥物生命週期中知識的創造方式、決策自動化的實現方式以及風險管理的管理方式。因此，相關人員必須從多個觀點看待人工智慧：將其視為藥物發現中假設生成的催化劑、用於精準識別患者和最佳化臨床試驗的工具、用於監管合規的分析引擎以及用於提升供應鏈韌性的營運賦能者。

為了成功駕馭這種環境，領導者必須理解三個交織的動態因素。首先，運算能力、資料基礎架構和模型架構的進步正在拓展可解決的問題範圍。其次，特定領域平台和檢驗工作流程的成熟正在減少調查團隊和臨床團隊之間的整合摩擦。第三，法規和倫理期望隨著技術能力的提升而不斷發展，這使得可重複性、可解釋性和穩健檢驗的重要性日益凸顯。因此，製藥業對人工智慧的應用越來越受到以結果為導向的實施的驅動，這些實施強調週期時間、品質和以患者為中心的可衡量改進，而不是技術本身。

這項實施分析為更深入的思考奠定了基礎，重點闡述了其對研發總監、臨床營運總監、法規負責人、生產總監和商業主管的實際意義。它強調了跨職能管治、清晰的技術和資料整合藍圖以及平衡平台開發與有針對性的概念驗證(PoC)舉措的投資策略的必要性。簡而言之，隨著人工智慧從新興技術走向營運基礎，那些能夠將自身技術能力與臨床和法規目標相契合的組織將從中獲益匪淺。

透過人工智慧，對正在重新定義藥物發現、臨床實驗設計、監管參與、生產製造和病人參與的變革性技術和組織轉變進行深入、全面的分析。

製藥業的格局正在經歷一場變革性的轉變，其驅動力包括技術突破、組織思維模式的轉變以及外部政策的影響。在技​​術層面，模型架構、訓練方法和特定領域演算法的進步正在拓展自動化和預測的邊界。卷積類神經網路、生成對抗網路、循環神經網路和變壓器等深度學習創新，結合監督學習、無監督學習和強化學習等實用機器學習技術，使得解決複雜的生物醫學問題成為可能。同時，包括影像分割、醫學影像應用和目標檢測在內的電腦視覺技術，正在為診斷和臨床前測試分析開闢新的途徑。此外，自然語言處理技術，例如情緒分析、語音辨識和文字探勘，能夠從醫療記錄、監管文件和文獻中提取可操作的洞見。

在組織層面，我們看到一個明顯的轉變，即從孤立的概念驗證轉向整合軟體和服務產品的大規模部署。組件層面的細分錶明，從臨床試驗管理平台和診斷工具到藥物發現平台、法規遵循工具和供應鏈管理解決方案等軟體領域，都得到了包含託管服務和專業服務在內的服務生態系統的補充。這種服務和軟體的整合，透過將技術實施與領域專業知識結合，加快了價值實現的速度。同時，臨床試驗、藥物發現、個人化醫療和供應鏈最佳化等應用領域也正日趨成熟。臨床試驗自動化正在擴展到患者招募、臨床數據管理、預測分析和基於風險的監測，而藥物發現工作流程則正在整合電腦輔助先導化合物最佳化、標靶選擇和最終模型檢驗。

這些變化也正在推動人才和夥伴關係策略的重塑。生命科學公司正在投資組建跨學科團隊，這些團隊匯集了生物醫學研究人員、資料工程師和法規專家。受託研究機構(CRO) 和技術供應商正擴大與製藥公司合作，共同開發檢驗的工作流程，並確保其可重複性。這些技術和組織變革的結合，正在創造一個全新的競爭格局，在這個格局中，速度、嚴謹性和法規檢驗是關鍵的差異化因素。

重點分析2025年美國關稅對人工智慧驅動的醫藥供應鏈、跨境採購、生產投入和合作模式的累積影響

2025年的關稅格局為人工智慧驅動的製藥業務的採購、供應鏈規劃和跨境合作增添了更多複雜性。影響硬體進口、試劑採購、臨床設備和軟體許可的關稅可能會對整個生態系統產生連鎖反應。例如，提高專用運算硬體和實驗室設備的關稅可能會增加本地部署的總擁有成本，從而使將運算風險外包的雲端基礎解決方案更具經濟優勢。相反，針對某些軟體即服務 (SaaS) 模式或捆綁解決方案的關稅可能會使採購重點轉向模組化架構和在地化服務模式。

除了直接的成本影響外，關稅還會影響供應商的選擇和籌資策略。為了降低貿易政策變化帶來的風險，企業會採取多種應對措施，例如供應商多元化、加快對替代供應商的資格認證以及重新評估其區域製造地。這些調整通常會影響檢驗和監管申報的時間表，因為引入新供應商和不同設備會增加額外的文件負擔。此外，由於企業希望減少對關鍵零件和生物製藥生產投入品的跨境依賴，關稅也可能影響企業在近岸外包和回流生產方面的投資決策。

此外，關稅也將影響跨境合作研究和資料共用安排。海關檢查力度加大以及進口法規的變更可能會使合作試驗所需的生物樣本、專用試劑和設備的運輸變得更加複雜。跨國專案可能需要贊助公司重新設計物流路線、重新評估第三方供應商契約，並更新風險登記冊以反映關稅相關的延誤。為此，具有前瞻性的機構正在將供應鏈透明度、多源合格和合約靈活性作為其業務永續營運計劃的優先事項。雖然關稅不會改變人工智慧應用的科學可行性，但它們會對這些應用的部署和規模化營運路徑產生重大影響。

綜合細分分析，解釋組件、技術系列、治療領域、應用領域、實施模型和最終用戶概況如何相互關聯，從而確定人工智慧在製藥業的應用和價值創造。

要了解人工智慧在製藥業創造價值的途徑和方式，需要對影響其應用模式和結果的多個細分維度進行統一的視角分析。基於組件的格局由服務和軟體構成。服務又可細分為託管服務和專業服務，而軟體則包括臨床試驗管理軟體、診斷軟體、藥物發現平台、法規遵循工具和供應鏈管理軟體。這種組件層面的觀點揭示了許多將軟體平台與上線和營運管理支援相結合的實際應用案例，旨在確保符合監管要求並保障業務連續性。

目錄

第1章：序言

第2章調查方法

第3章執行摘要

第4章 市場概覽

第5章 市場洞察

• 將生成式人工智慧應用於加速候選藥物的結構最佳化和合成規劃
• 應用聯邦學習框架實現安全的多機構藥物資料交換
• 引入人工智慧驅動的數位雙胞胎模型，用於臨床試驗中的個人化藥物動力學和動態模擬
• 開發可解釋的人工智慧演算法，以確保複雜藥物核准流程中的監管合規性
• 利用深度學習模型對靶向蛋白質-蛋白質相互作用的生物製藥進行高通量In Silico篩檢
• 利用人工智慧引導的機器人平台開發分析自動化高內涵細胞檢測方法
• 利用社群媒體和電子健康記錄（EHR）資料流，實施即時人工智慧藥物監測系統

第6章：美國關稅的累積影響，2025年

第7章：人工智慧的累積影響，2025年

第8章：按成分分類的藥品市場

• 服務
  • 託管服務
  • 專業服務
• 軟體
  • 臨床試驗管理軟體
  • 診斷軟體
  • 藥物發現平台
  • 監理合規工具
  • 供應鏈管理軟體

第9章：依科技分類的醫藥市場

• 電腦視覺
  • 影像分割
  • 醫學影像
  • 目標偵測
• 深度學習
  • 卷積類神經網路
  • 生成對抗網路
  • 循環神經網路
  • 變壓器
• 機器學習
  • 強化學習
  • 監督式學習
  • 無監督學習
• 自然語言處理
  • 情緒分析
  • 語音辨識
  • 文字探勘
• 機器人流程自動化

第10章：依治療領域分類的藥品市場

• 循環系統疾病
• 免疫學
• 感染疾病
• 代謝性疾病
• 神經病學
• 腫瘤學
• 呼吸系統疾病

第11章 按應用分類的醫藥市場

• 臨床試驗
  • 臨床數據管理
  • 病患招募
  • 預測分析
  • 基於風險的監測
• 藥物發現
  • 藥物發現設計
  • 結束模型檢驗
  • 先導藥物最適化
  • 目標選擇
• 個人化醫療
  • 生物標記發現
  • 基因組分析
  • 精準醫療發展
• 供應鏈管理
  • 需求預測
  • 庫存管理
  • 物流最佳化

第12章 依部署類型分類的藥品市場

• 雲端基礎的
• 本地部署

第13章：依最終用戶分類的藥品市場

• 學術和研究機構
• 合約研究組織（CRO）
• 製藥和生物技術公司

第14章：各地區的醫藥市場

• 美洲
  • 北美洲
  • 拉丁美洲
• 歐洲、中東和非洲
  • 歐洲
  • 中東
  • 非洲
• 亞太地區

第15章：依組別分類的藥品市場

• ASEAN
• GCC
• EU
• BRICS
• G7
• NATO

第16章：各國藥品市場

• 美國
• 加拿大
• 墨西哥
• 巴西
• 英國
• 德國
• 法國
• 俄羅斯
• 義大利
• 西班牙
• 中國
• 印度
• 日本
• 澳洲
• 韓國

第17章 競爭格局

• 2024年市佔率分析
• FPNV定位矩陣，2024
• 競爭分析
  • AiCure, LLC
  • Aspen Technology Inc.
  • Atomwise Inc.
  • BenevolentAI SA
  • BioSymetrics Inc.
  • BPGbio Inc.
  • Butterfly Network, Inc.
  • Cloud Pharmaceuticals, Inc.
  • Cyclica by Recursion Pharmaceuticals, Inc.
  • Deargen Inc.
  • Deep Genomics Incorporated
  • Deloitte Touche Tohmatsu Limited
  • Euretos Services BV
  • Exscientia PLC
  • Insilico Medicine
  • Intel Corporation
  • International Business Machines Corporation
  • InveniAI LLC
  • Isomorphic Labs Limited
  • Microsoft Corporation
  • Novo Nordisk A/S
  • NVIDIA Corporation
  • Oracle Corporation
  • SANOFI WINTHROP INDUSTRIE
  • Turbine Ltd.
  • Viseven Europe OU
  • XtalPi Inc.

The Artificial Intelligence in Pharmaceutical Market is projected to grow by USD 111.13 billion at a CAGR of 27.61% by 2032.

A strategic introduction outlining how artificial intelligence is reshaping pharmaceutical research, clinical operations, regulatory pathways, manufacturing workflows, and commercial strategies

Artificial intelligence is no longer an experimental adjunct in pharmaceutical workstreams; it has become an integral strategic capability that touches discovery science, clinical development, regulatory strategy, manufacturing operations, and commercial decision-making. This introduction frames AI not merely as a set of technologies but as a system-level force reshaping how knowledge is generated, decisions are automated, and risks are managed across the lifecycle of medicines. Stakeholders must therefore view AI through multiple lenses: as an accelerant for hypothesis generation in drug discovery, as a precision tool for patient identification and trial optimization, as an analytics engine for regulatory compliance, and as an operational enabler for supply chain resilience.

To navigate this environment, leaders must appreciate three converging dynamics. First, advances in compute, data infrastructure, and model architectures are broadening the range of tractable problems. Second, the maturation of domain-specific platforms and validated workflows is lowering integration friction for research and clinical teams. Third, regulatory and ethical expectations are co-evolving with capabilities, increasing the importance of reproducibility, explainability, and robust validation. As a result, AI adoption in pharmaceuticals is increasingly driven by outcome-oriented deployments that emphasize measurable improvements in cycle time, quality, and patient-centricity rather than technology for its own sake.

This introductory analysis sets the stage for deeper examination by emphasizing practical implications for R&D leaders, clinical operations directors, regulatory strategists, manufacturing heads, and commercial executives. It establishes the imperative for cross-functional governance, a clear technology- and data-integration roadmap, and an investment posture that balances platform development with targeted proof-of-concept initiatives. In short, organizations that align technical capability with clinical and regulatory objectives are positioned to capture disproportionate value as AI transitions from novelty to operational backbone.

A detailed synthesis of transformative technological and organizational shifts that are redefining drug discovery, trial design, regulatory interactions, manufacturing, and patient engagement through AI

The pharmaceutical landscape is undergoing transformative shifts driven by technological breakthroughs, shifting organizational mindsets, and external policy influences. At the technology level, advances in model architectures, training regimes, and domain-adapted algorithms are expanding the frontier of what can be automated and predicted. Deep learning innovations in convolutional neural networks, generative adversarial networks, recurrent neural networks, and transformers are increasingly coupled with pragmatic machine learning approaches such as supervised and unsupervised learning plus reinforcement learning to address complex biomedical problems. In parallel, computer vision capabilities including image segmentation, medical imaging applications, and object detection are unlocking new modalities for diagnostics and preclinical assay analysis, while natural language processing is enabling extraction of actionable insights from clinical notes, regulatory submissions, and literature through techniques such as sentiment analysis, speech recognition, and text mining.

Organizationally, there is a clear shift from isolated proofs of concept to scaled deployments that integrate software and service offerings. Component-level segmentation illustrates that software domains-ranging from clinical trial management platforms and diagnostic tools to drug discovery platforms, regulatory compliance tools, and supply chain management solutions-are being complemented by services ecosystems that include managed and professional services. This integration of services and software is accelerating time-to-value by combining technical implementation with domain expertise. Simultaneously, application domains such as clinical trials, drug discovery, personalized healthcare, and supply chain optimization are maturing; clinical trial automation is extending into patient recruitment, clinical data management, predictive analytics, and risk-based monitoring, while drug discovery workflows are embracing computational drug design, lead optimization, target selection, and end-model validation.

These shifts are also reshaping talent and partnership strategies. Life sciences organizations are investing in cross-disciplinary teams that combine biomedical scientists, data engineers, and regulatory specialists. Contract research organizations and technology vendors are forming deeper alliances with pharmaceutical companies to co-develop validated workflows and to ensure reproducibility. Taken together, these technical and organizational transformations are creating a new competitive dynamic where speed, rigor, and regulatory-aligned validation are the primary differentiators.

A focused analysis of the cumulative effects of United States tariff measures enacted in 2025 on AI-enabled pharmaceutical supply chains, cross-border sourcing, manufacturing inputs, and collaboration models

The tariff landscape introduced in 2025 has introduced additional complexity into procurement, supply chain planning, and cross-border collaboration for AI-enabled pharmaceutical operations. Tariff measures that affect hardware imports, reagent sourcing, clinical instrumentation, and software licensing can create ripple effects across the ecosystem. For example, increases in duties on specialized computing hardware or laboratory instrumentation raise the total cost of ownership for on-premises deployments and may tilt the economics in favor of cloud-based solutions where compute risks can be externalized. Conversely, tariffs that target certain software-as-a-service models or bundled solutions can shift procurement preferences toward modular architectures and localized service models.

Beyond direct cost impacts, tariffs affect supplier selection and sourcing strategies. Organizations respond by diversifying supplier bases, accelerating qualification of alternative vendors, and re-evaluating regional manufacturing footprints to mitigate exposure to trade policy shifts. These adjustments often influence timelines for validation and regulatory filings, because change controls associated with new suppliers or different equipment can introduce additional documentation burdens. In addition, tariffs can influence investment decisions in nearshoring or reshoring initiatives, where companies seek to reduce cross-border dependencies for critical components or biologics manufacturing inputs.

Moreover, tariffs have implications for collaborative research and data-sharing arrangements across borders. Increased customs scrutiny and shifting import regimes can complicate the transport of biological samples, specialized reagents, and equipment essential for collaborative trials. For multinational programs, sponsors may need to redesign logistics corridors, re-assess third-party provider contracts, and update risk registers to reflect tariff-induced delays. In response, savvy organizations are prioritizing supply chain visibility, multi-source qualification, and contractual flexibility as part of their operational resilience programs. While tariffs do not alter the scientific feasibility of AI applications, they meaningfully affect the operational pathways through which those applications are deployed and scaled.

Integrated segmentation insights explaining how components, technology families, therapeutic focus areas, application domains, deployment models, and end-user profiles jointly determine AI uptake and value creation in pharma

Understanding where and how AI generates value in pharmaceuticals requires an integrated view of multiple segmentation axes that together shape adoption patterns and outcomes. Based on Component, the landscape comprises Services and Software where Services splits into Managed Services and Professional Services and Software includes clinical trial management software, diagnostic software, drug discovery platforms, regulatory compliance tools, and supply chain management software. This component-level view clarifies that practical deployments frequently combine software platforms with implementation and managed support to ensure regulatory-grade performance and operational continuity.

Based on Technology, adopters must evaluate capabilities across computer vision, deep learning, machine learning, natural language processing, and robotic process automation; within these families there are important sub-specializations such as image segmentation, medical imaging, and object detection for computer vision, convolutional neural networks, generative adversarial networks, recurrent neural networks, and transformers for deep learning, and reinforcement learning, supervised learning, and unsupervised learning for machine learning, alongside sentiment analysis, speech recognition, and text mining for NLP. The multiplicity of approaches underscores the need for a technology taxonomy that maps each method to specific use cases and validation requirements.

Based on Therapeutic Area, AI initiatives often align with clinical priority and data maturity across cardiovascular diseases, immunology, infectious diseases, metabolic diseases, neurology, oncology, and respiratory diseases. Disease biology, endpoint definability, and data availability vary across these areas, which in turn affects algorithmic approachability and regulatory scrutiny. Based on Applications, deployment domains include clinical trials, drug discovery, personalized healthcare, and supply chain management with clinical trials subdividing into clinical data management, patient recruitment, predictive analytics, and risk-based monitoring, while drug discovery encompasses drug design, end-model validation, lead optimization, and target selection and personalized healthcare covers biomarker discovery, genomic profiling, and precision medicine development and supply chain management focuses on demand forecasting, inventory management, and logistics optimization.

Based on Deployment Type, choices between cloud-based and on-premises architectures have implications for data governance, latency, and cost structure, and based on End User, the primary consumers of these solutions span academic and research institutions, contract research organizations, and pharmaceutical and biotechnology companies. The intersections among these segmentation axes create contextual trade-offs: for example, oncology discovery efforts may preferentially adopt deep learning generative models and on-premises deployments when patient-level privacy and validation are paramount, while supply chain optimization workstreams commonly leverage cloud-based machine learning and managed services to maximize elasticity and cross-site visibility. Therefore, segmentation-aware strategies are essential to align technical design, validation planning, procurement strategy, and organizational capability development.

Comprehensive regional insights comparing strategic drivers, regulatory landscapes, investment trends, infrastructure readiness, and commercial adoption patterns across Americas, Europe Middle East & Africa, and Asia-Pacific

Regional dynamics exert a strong influence on how AI is adopted and scaled across the pharmaceutical value chain, with distinctive patterns emerging across the Americas, Europe, Middle East & Africa, and Asia-Pacific. In the Americas, a robust private sector investment environment, advanced cloud infrastructure, and established venture ecosystems have accelerated platform development and commercial deployments, while regulatory guidance in certain jurisdictions has moved toward outcomes-based validation and clearer frameworks for software as a medical device. This creates a favorable environment for companies that combine rapid iteration with strong evidence-generation capabilities.

In Europe, Middle East & Africa, regulatory rigor and data protection regimes shape design and deployment choices, often increasing the emphasis on explainability, localized data residency, and formalized validation paths. National policy initiatives and pan-European collaborations have also fostered consortium-based models for data sharing that enable multicenter trials and federated learning approaches. Meanwhile in parts of the Middle East and Africa, infrastructural variability and nascent data ecosystems require bespoke implementation models and capacity-building partnerships.

Asia-Pacific presents a heterogeneous but highly dynamic set of conditions where strong manufacturing clusters, rapidly growing clinical trial activity, and sizable patient populations create compelling use cases for AI. Several markets in the region are advancing digital health policies and public-private partnerships that accelerate deployment of diagnostic and clinical decision-support tools. Importantly, regional supply chain integration, proximity to key hardware suppliers, and an expanding talent base make Asia-Pacific an attractive locus for both development and scaled implementation projects. Across all regions, local regulatory expectations, talent availability, data governance frameworks, and infrastructure maturity determine the optimal mix of cloud versus on-premises deployment and the most effective partnership models for vendors and sponsors alike.

Company insights emphasizing competitive positioning, partnership strategies, product and service portfolios, platform differentiation, and go-to-market approaches among leading AI players supporting pharmaceutical innovation

Company behavior in the AI-for-pharma ecosystem demonstrates distinct strategic archetypes, including platform providers that invest in end-to-end product suites, specialized algorithm developers focusing on narrow high-value use cases, systems integrators that bridge domain expertise with scalable implementation, and contract research organizations that embed AI capabilities into outsourced development services. Leading organizations are differentiating through validated data assets, regulatory-compliant workflows, and capabilities that reduce integration friction for life sciences customers.

Across supplier strategies, we observe three persistent patterns. The first is platform consolidation where vendors expand horizontally to offer clinical trial, discovery, and compliance modules that interoperate within a single architecture. The second is vertical specialization where niche players concentrate on a therapeutic or modality-specific problem-such as imaging in oncology or genomic profiling in personalized medicine-and achieve deep validation within that domain. The third pattern is partnership ecosystems where companies join forces to combine proprietary algorithms, clinical data, and laboratory automation in order to deliver regulated outcomes.

From a procurement perspective, pharmaceutical and biotechnology customers increasingly evaluate vendors on evidence of real-world performance, regulatory readiness, and post-deployment support capabilities, rather than on feature checklists alone. As a result, successful companies prioritize clinical validation studies, transparent model governance, and comprehensive professional or managed services to ensure sustained operational performance. Contracts reflect these expectations with outcomes-linked milestones, change-control provisions, and clear responsibilities for data stewardship and model maintenance.

Actionable strategic recommendations for executives to accelerate responsible AI adoption by prioritizing governance, talent development, ecosystem partnerships, platform selection, and regulatory alignment across functions

Industry leaders seeking to accelerate responsible and strategic AI adoption should pursue a coherent mix of governance, talent, technology, and partnership actions. Begin by establishing cross-functional governance that assigns clear accountability for model development, validation, deployment, and monitoring; governance structures should integrate legal, regulatory, clinical, and technical stakeholders and define standardized validation protocols and audit trails to satisfy regulators and internal risk functions. Simultaneously, invest in talent programs that blend domain expertise with data science skills; rotational programs, embedded data scientists within therapeutic teams, and strategic hiring of regulatory-savvy machine learning engineers will shorten feedback loops and improve the alignment of algorithms with clinical objectives.

On the technology front, prioritize modular architectures that balance the benefits of cloud-based scalability with the control afforded by on-premises deployments where privacy or latency constraints demand it. Adopt open and transparent model governance practices, including versioning, reproducibility tests, and clear explainability artifacts tied to clinical endpoints. In parallel, develop an ecosystem strategy that differentiates between capabilities to build internally and those best accessed through partnerships with academic centers, CROs, or specialized vendors. Structured collaborations with contract research organizations can accelerate trial execution, while alliances with diagnostic firms and lab automation providers can de-risk end-to-end implementation.

Finally, align procurement and contracting approaches with performance-based outcomes and continuous validation requirements. Include provisions for post-deployment monitoring, change management, and retraining cycles in vendor agreements. Taken together, these steps provide a pragmatic roadmap for leaders to scale AI responsibly while delivering measurable clinical and operational improvements.

A transparent research methodology describing data sources, stakeholder interviews, technology taxonomy alignment, therapeutic mapping, validation processes, and analytical frameworks used to generate conclusions

The conclusions and insights in this report are grounded in a multi-method research approach combining primary and secondary evidence, expert interviews, and a technology- and therapeutic-focused taxonomy to ensure applicability across decision contexts. Data collection included structured discussions with interdisciplinary stakeholders across pharmaceutical companies, biotechnology firms, contract research organizations, clinical investigators, regulatory specialists, and technology vendors to validate practical constraints, preferred validation strategies, and deployment models. Secondary inputs comprised peer-reviewed literature, regulatory guidance documents, standards for software as a medical device, and public technical disclosures that inform model architectures and validation practices.

Analytically, the work uses a taxonomy that maps component types, technology families, therapeutic priorities, application domains, deployment models, and end-user segments to observed adoption patterns and implementation risks. Validation exercises included cross-referencing interview findings with documented case studies and technology white papers, and applying scenario analysis to explore the operational consequences of supply chain disruptions and policy changes. Quality assurance measures involved iterative peer review, triangulation of evidence across sources, and explicit documentation of assumptions and limitations. This methodology ensures that recommendations are traceable to observable practices and that the analytical framework remains adaptable to evolving regulatory and technical developments.

A concise conclusion synthesizing implications for R&D, clinical operations, regulatory strategy, commercial deployment, and supply chain resilience while emphasizing governance, skills, and collaboration imperatives

The cumulative analysis underscores a singular strategic reality: artificial intelligence is now a foundational capability for pharmaceutical organizations that seek to improve R&D productivity, enhance clinical trial efficiency, strengthen regulatory compliance, and optimize supply chain resilience. Success in this era depends not on chasing every technical novelty but on disciplined alignment of AI investments with clinical and regulatory priorities, rigorous validation practices, and robust operational governance. Organizations that combine domain-focused model development with partnerships that supply complementary data, lab automation, and implementation expertise will move faster from prototype to production.

Moreover, the interplay between policy levers-such as tariffs and data governance regimes-and operational execution highlights the need for continuous risk assessment and adaptive sourcing strategies. Effective deployment requires a pragmatic mix of cloud and on-premises approaches informed by privacy constraints and latency considerations, and contracting models that emphasize outcomes and post-deployment stewardship. Ultimately, building sustained advantage with AI in pharmaceuticals is a multi-year endeavor that hinges on reproducibility, explainability, and the capacity to learn from real-world performance. Executives who prioritize these elements will be positioned to convert technical capability into measurable clinical and business results.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Integration of generative AI for accelerated drug candidate structure optimization and synthesis planning
• 5.2. Application of federated learning frameworks for secure multi-center pharmaceutical data collaboration
• 5.3. Deployment of AI-driven digital twin models for personalized pharmacokinetic and dynamic simulations in trials
• 5.4. Development of explainable AI algorithms to ensure regulatory compliance in complex drug approval workflows
• 5.5. Adoption of deep learning models for high-throughput in silico screening of biologics targeting protein-protein interactions
• 5.6. Utilization of AI-guided robotic platforms for automated high-content cell-based assay development and analysis
• 5.7. Implementation of real-time AI-enabled pharmacovigilance systems leveraging social media and EHR data streams

6. Cumulative Impact of United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. Artificial Intelligence in Pharmaceutical Market, by Component

• 8.1. Services
  • 8.1.1. Managed Services
  • 8.1.2. Professional Services
• 8.2. Software
  • 8.2.1. Clinical Trial Management Software
  • 8.2.2. Diagnostic Software
  • 8.2.3. Drug Discovery Platforms
  • 8.2.4. Regulatory Compliance Tools
  • 8.2.5. Supply Chain Management Software

9. Artificial Intelligence in Pharmaceutical Market, by Technology

• 9.1. Computer Vision
  • 9.1.1. Image Segmentation
  • 9.1.2. Medical Imaging
  • 9.1.3. Object Detection
• 9.2. Deep Learning
  • 9.2.1. Convolutional Neural Networks
  • 9.2.2. Generative Adversarial Networks
  • 9.2.3. Recurrent Neural Networks
  • 9.2.4. Transformers
• 9.3. Machine Learning
  • 9.3.1. Reinforcement Learning
  • 9.3.2. Supervised Learning
  • 9.3.3. Unsupervised Learning
• 9.4. Natural Language Processing
  • 9.4.1. Sentiment Analysis
  • 9.4.2. Speech Recognition
  • 9.4.3. Text Mining
• 9.5. Robotic Process Automation

10. Artificial Intelligence in Pharmaceutical Market, by Therapeutic Area

• 10.1. Cardiovascular Diseases
• 10.2. Immunology
• 10.3. Infectious Diseases
• 10.4. Metabolic Diseases
• 10.5. Neurology
• 10.6. Oncology
• 10.7. Respiratory Diseases

11. Artificial Intelligence in Pharmaceutical Market, by Applications

• 11.1. Clinical Trials
  • 11.1.1. Clinical Data Management
  • 11.1.2. Patient Recruitment
  • 11.1.3. Predictive Analytics
  • 11.1.4. Risk-Based Monitoring
• 11.2. Drug Discovery
  • 11.2.1. Drug Design
  • 11.2.2. End-Model Validation
  • 11.2.3. Lead Optimization
  • 11.2.4. Target Selection
• 11.3. Personalized Healthcare
  • 11.3.1. Biomarker Discovery
  • 11.3.2. Genomic Profiling
  • 11.3.3. Precision Medicine Development
• 11.4. Supply Chain Management
  • 11.4.1. Demand Forecasting
  • 11.4.2. Inventory Management
  • 11.4.3. Logistics Optimization

12. Artificial Intelligence in Pharmaceutical Market, by Deployment Type

• 12.1. Cloud-Based
• 12.2. On-Premises

13. Artificial Intelligence in Pharmaceutical Market, by End User

• 13.1. Academic and Research Institutions
• 13.2. Contract Research Organizations (CROs)
• 13.3. Pharmaceutical & Biotechnology Companies

14. Artificial Intelligence in Pharmaceutical Market, by Region

• 14.1. Americas
  • 14.1.1. North America
  • 14.1.2. Latin America
• 14.2. Europe, Middle East & Africa
  • 14.2.1. Europe
  • 14.2.2. Middle East
  • 14.2.3. Africa
• 14.3. Asia-Pacific

15. Artificial Intelligence in Pharmaceutical Market, by Group

• 15.1. ASEAN
• 15.2. GCC
• 15.3. European Union
• 15.4. BRICS
• 15.5. G7
• 15.6. NATO

16. Artificial Intelligence in Pharmaceutical Market, by Country

• 16.1. United States
• 16.2. Canada
• 16.3. Mexico
• 16.4. Brazil
• 16.5. United Kingdom
• 16.6. Germany
• 16.7. France
• 16.8. Russia
• 16.9. Italy
• 16.10. Spain
• 16.11. China
• 16.12. India
• 16.13. Japan
• 16.14. Australia
• 16.15. South Korea

17. Competitive Landscape

• 17.1. Market Share Analysis, 2024
• 17.2. FPNV Positioning Matrix, 2024
• 17.3. Competitive Analysis
  • 17.3.1. AiCure, LLC
  • 17.3.2. Aspen Technology Inc.
  • 17.3.3. Atomwise Inc.
  • 17.3.4. BenevolentAI SA
  • 17.3.5. BioSymetrics Inc.
  • 17.3.6. BPGbio Inc.
  • 17.3.7. Butterfly Network, Inc.
  • 17.3.8. Cloud Pharmaceuticals, Inc.
  • 17.3.9. Cyclica by Recursion Pharmaceuticals, Inc.
  • 17.3.10. Deargen Inc.
  • 17.3.11. Deep Genomics Incorporated
  • 17.3.12. Deloitte Touche Tohmatsu Limited
  • 17.3.13. Euretos Services BV
  • 17.3.14. Exscientia PLC
  • 17.3.15. Insilico Medicine
  • 17.3.16. Intel Corporation
  • 17.3.17. International Business Machines Corporation
  • 17.3.18. InveniAI LLC
  • 17.3.19. Isomorphic Labs Limited
  • 17.3.20. Microsoft Corporation
  • 17.3.21. Novo Nordisk A/S
  • 17.3.22. NVIDIA Corporation
  • 17.3.23. Oracle Corporation
  • 17.3.24. SANOFI WINTHROP INDUSTRIE
  • 17.3.25. Turbine Ltd.
  • 17.3.26. Viseven Europe OU
  • 17.3.27. XtalPi Inc.

LIST OF FIGURES

• FIGURE 1. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, 2018-2032 (USD MILLION)
• FIGURE 2. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPONENT, 2024 VS 2032 (%)
• FIGURE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPONENT, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TECHNOLOGY, 2024 VS 2032 (%)
• FIGURE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TECHNOLOGY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY THERAPEUTIC AREA, 2024 VS 2032 (%)
• FIGURE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY THERAPEUTIC AREA, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY APPLICATIONS, 2024 VS 2032 (%)
• FIGURE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY APPLICATIONS, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEPLOYMENT TYPE, 2024 VS 2032 (%)
• FIGURE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEPLOYMENT TYPE, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 12. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY END USER, 2024 VS 2032 (%)
• FIGURE 13. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY END USER, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 14. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 15. AMERICAS ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUBREGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 16. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 17. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 18. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUBREGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 19. EUROPE ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 20. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 21. AFRICA ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 22. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 23. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GROUP, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 24. ASEAN ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 25. GCC ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 26. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 27. BRICS ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 28. G7 ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 29. NATO ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 30. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 31. ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SHARE, BY KEY PLAYER, 2024
• FIGURE 32. ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET, FPNV POSITIONING MATRIX, 2024

LIST OF TABLES

• TABLE 1. ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SEGMENTATION & COVERAGE
• TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2024
• TABLE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, 2018-2024 (USD MILLION)
• TABLE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, 2025-2032 (USD MILLION)
• TABLE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPONENT, 2018-2024 (USD MILLION)
• TABLE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPONENT, 2025-2032 (USD MILLION)
• TABLE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, 2018-2024 (USD MILLION)
• TABLE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, 2025-2032 (USD MILLION)
• TABLE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 12. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 13. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 14. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 15. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 16. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 17. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 18. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 19. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 20. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 21. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 22. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 23. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 24. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 25. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 26. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 27. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, 2018-2024 (USD MILLION)
• TABLE 28. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, 2025-2032 (USD MILLION)
• TABLE 29. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 30. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 31. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 32. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 33. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 34. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 35. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 36. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 37. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 38. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 39. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 40. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 41. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 42. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 43. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 44. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 45. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 46. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 47. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 48. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 49. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 50. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 51. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 52. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 53. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 54. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 55. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 56. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 57. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 58. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 59. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 60. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 61. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 62. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 63. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 64. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 65. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TECHNOLOGY, 2018-2024 (USD MILLION)
• TABLE 66. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TECHNOLOGY, 2025-2032 (USD MILLION)
• TABLE 67. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, 2018-2024 (USD MILLION)
• TABLE 68. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, 2025-2032 (USD MILLION)
• TABLE 69. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 70. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 71. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 72. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 73. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 74. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 75. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 76. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 77. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 78. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 79. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 80. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 81. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 82. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 83. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 84. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 85. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 86. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 87. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 88. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 89. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 90. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 91. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 92. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 93. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, 2018-2024 (USD MILLION)
• TABLE 94. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, 2025-2032 (USD MILLION)
• TABLE 95. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 96. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 97. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 98. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 99. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 100. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 101. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 102. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 103. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 104. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 105. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 106. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 107. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 108. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 109. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 110. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 111. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 112. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 113. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 114. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 115. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 116. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 117. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 118. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 119. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 120. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 121. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 122. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 123. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 124. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 125. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, 2018-2024 (USD MILLION)
• TABLE 126. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, 2025-2032 (USD MILLION)
• TABLE 127. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 128. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 129. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 130. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 131. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 132. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 133. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 134. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 135. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 136. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 137. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 138. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 139. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 140. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 141. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 142. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 143. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 144. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 145. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 146. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 147. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 148. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 149. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 150. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 151. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2024 (USD MILLION)
• TABLE 152. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2025-2032 (USD MILLION)
• TABLE 153. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 154. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 155. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 156. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 157. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 158. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 159. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 160. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 161. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 162. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 163. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 164. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 165. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 166. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 167. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 168. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 169. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 170. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 171. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 172. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 173. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 174. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 175. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 176. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 177. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 178. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 179. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 180. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 181. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 182. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 183. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY THERAPEUTIC AREA, 2018-2024 (USD MILLION)
• TABLE 184. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY THERAPEUTIC AREA, 2025-2032 (USD MILLION)
• TABLE 185. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 186. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 187. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 188. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 189. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 190. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 191. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY REGION, 2018-2024 (USD MILLION)
• TABLE 192. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY REGION, 2025-2032 (USD MILLION)
• TABLE 193. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 194. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 195. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 196. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 197. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 198. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 199. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 200. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 201. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 202. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 203. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 204. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 205. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 206. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 207. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 208. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 209. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY REGION, 2018-2024 (USD MILLION)
• TABLE 210. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY REGION, 2025-2032 (USD MILLION)
• TABLE 211. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 212. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 213. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 214. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 215. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY REGION, 2018-2024 (USD MILLION)
• TABLE 216. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY REGION, 2025-2032 (USD MILLION)
• TABLE 217. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 218. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 219. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 220. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 221. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 222. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 223. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 224. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 225. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 226. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 227. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY APPLICATIONS, 2018-2024 (USD MILLION)
• TABLE 228. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY APPLICATIONS, 2025-2032 (USD MILLION)
• TABLE 229. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, 2018-2024 (USD MILLION)
• TABLE 230. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, 2025-2032 (USD MILLION)
• TABLE 231. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 232. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 233. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 234. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 235. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 236. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 237. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY REGION, 2018-2024 (USD MILLION)
• TABLE 238. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY REGION, 2025-2032 (USD MILLION)
• TABLE 239. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 240. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 241. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 242. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 243. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY REGION, 2018-2024 (USD MILLION)
• TABLE 244. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY REGION, 2025-2032 (USD MILLION)
• TABLE 245. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 246. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 247. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 248. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 249. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PREDICTIVE ANALYTICS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 250. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PREDICTIVE ANALYTICS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 251. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PREDICTIVE ANALYTICS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 252. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PREDICTIVE ANALYTICS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 253. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PREDICTIVE ANALYTICS, BY COUNTRY, 2018-2024 (USD MILLION)

TABLE 254.