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市場調查報告書
商品編碼
1754032
2025 年至 2033 年生命科學領域人工智慧市場報告(按產品、部署、應用和地區)Artificial Intelligence in Life Sciences Market Report by Offering, Deployment, Application, and Region 2025-2033 |
2024年,全球生命科學領域人工智慧市場規模達29億美元。展望未來, IMARC Group預計到2033年,該市場規模將達到167億美元,2025-2033年期間的複合年成長率(CAGR)為21.5%。複雜疾病盛行率的上升、人工智慧在醫學影像分析中的應用日益廣泛、人工智慧與基因組學研究和分析的融合,以及人工智慧與新興技術的融合,是推動市場發展的主要因素。
加速藥物研發
傳統的藥物開發過程漫長、昂貴,而且往往效率低下,一種新藥需要十多年才能推向市場。人工智慧透過加快藥物開發的各個階段改變了這一狀況。例如,2023 年,Cognizant 在舊金山成立了先進人工智慧 (AI) 實驗室,主要致力於核心人工智慧研究、創新和尖端人工智慧系統的開發。該實驗室由一支敬業的人工智慧研究人員和開發人員組成,已經擁有 75 項已發布和待批專利,並將與研究機構、客戶和新創公司合作。機器學習演算法分析大量資料集,包括生物和化學資訊、臨床試驗資料和現有藥物資料庫,以前所未有的速度和準確度識別潛在的候選藥物。這使研究人員能夠精確定位有前景的化合物,預測其功效並最佳化其特性,大大減少藥物發現所需的時間和成本,從而推動生命科學市場中的人工智慧成長。
個人化醫療和醫療保健
傳統醫療通常採用一刀切的方案,根據廣泛的人口平均值開出藥物和療法。人工智慧利用巨量資料和機器學習的力量,分析個人的基因組成、臨床病史、生活方式因素和即時健康資料,從而製定高度客製化的治療方案。 2023年,OM1推出了PhenOM,這是一個基於人工智慧的個人化醫療平台,利用豐富的醫療資料集和人工智慧技術。 PhenOM使用縱向健康史資料進行校準,識別與疾病相關的獨特數位表現型,從而實現大規模個人化醫療洞察。 OM1專注於慢性病,率先進行創新的RWE研究,為患者治療結果提供個人化影響,並透過尖端人工智慧解決方案推動醫療保健發展。這種程度的個人化確保患者接受的治療不僅更有效,而且不太可能引起不良副作用。此外,人工智慧驅動的預測模型可以幫助識別某些疾病風險較高的患者,從而實現早期介入和預防措施。此外,在腫瘤學領域,人工智慧有助於找出導致患者癌症的特定基因突變,使腫瘤學家能夠推薦更有可能成功的標靶療法。
疾病診斷和生物標記發現
人工智慧演算法可以以卓越的準確性和效率分析各種醫療資料來源,包括醫學影像(例如X光片、核磁共振成像 (MRI) 和 CT 掃描)、患者電子健康記錄和基因組圖譜。在放射學領域,人工智慧影像分析可以幫助放射科醫生發現細微的異常並標記潛在的健康問題,有助於早期診斷和治療。 2024年,Rad AI 與Google合作,利用人工智慧技術增強放射學報告,旨在節省放射科醫生的時間、減少倦怠並提高患者護理品質。此次合作將簡化工作流程、自動化重複性任務,並提高放射學報告的效率和準確性。此外,人工智慧在疾病生物標記的發現中發揮著重要作用,這對於在早期階段識別疾病並監測其進展至關重要。機器學習模型可以檢測分子資料中的細微模式,幫助識別與各種疾病(包括癌症、阿茲海默症和心血管疾病)相關的特定生物標記。這些生物標記作為早期預警訊號,可以指導臨床醫生及時做出明智的病患照護決策。
The global artificial intelligence in life sciences market size reached USD 2.9 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 16.7 Billion by 2033, exhibiting a growth rate (CAGR) of 21.5% during 2025-2033. The rising prevalence of complex diseases, the increasing adoption of AI in medical imaging analysis, the integration of AI into genomics research and analysis, and the convergence of AI with emerging technologies are some of the major factors propelling the market.
Drug Discovery and Development Acceleration
The traditional drug development process is a lengthy, costly, and often inefficient endeavour, taking over a decade to bring a new drug into the market. AI transforms this landscape by expediting various stages of drug development. For instance, in 2023, Cognizant launched an Advanced Artificial Intelligence (AI) Lab in San Francisco to mainly focus on core AI research, innovation, and development of cutting-edge AI systems. The lab, staffed by a team of dedicated AI researchers and developers, has already produced 75 issued and pending patents and will collaborate with research institutions, customers, and startups. Machine learning algorithms analyse vast datasets, including biological and chemical information, clinical trial data, and existing drug databases, to identify potential drug candidates with unprecedented speed and accuracy. This enables researchers to pinpoint promising compounds, predict their efficacy, and optimize their properties, significantly reducing the time and cost required for drug discovery, thereby propelling the artificial intelligence in life sciences market growth.
Personalized Medicine and Healthcare
Traditional medical treatments often follow a one-size-fits-all approach, with medications and therapies prescribed based on broad population averages. AI harnesses the power of big data and machine learning to analyze an individual's genetic makeup, clinical history, lifestyle factors, and real-time health data to develop highly tailored treatment plans. In 2023, OM1 introduced PhenOM, an AI-powered platform for personalized medicine, leveraging enriched healthcare datasets and AI technology. Calibrated using longitudinal health history data, PhenOM identifies unique digital phenotypes associated with conditions, enabling personalized healthcare insights at scale. With a focus on chronic conditions, OM1 pioneers innovative RWE research, delivering personalized impact on patient outcomes and advancing healthcare through cutting-edge AI solutions.This level of personalization ensures that patients receive treatments that are not only more effective but also less likely to cause adverse side effects. Also, AI-driven predictive models can help identify patients at higher risk of certain diseases, allowing for early intervention and preventive measures. Additionally, in oncology, AI assists in pinpointing the specific genetic mutations driving a patient's cancer, enabling oncologists to recommend targeted therapies that are more likely to be successful.
Disease Diagnosis and Biomarker Discovery
AI algorithms can analyze diverse medical data sources, including medical images, such as X-rays, MRIs, and CT scans, patient electronic health records, and genomic profiles, with exceptional accuracy and efficiency. In radiology, AI-powered image analysis can assist radiologists in detecting subtle abnormalities and flagging potential health issues, aiding in early diagnosis and treatment. In 2024, Rad AI has partnered with Google to enhance radiology reporting by leveraging AI technology, aiming to save radiologists time, reduce burnout, and improve patient care quality. This collaboration will streamline workflows, automate repetitive tasks, and advance the efficiency and accuracy of radiology reporting. Moreover, AI is instrumental in the discovery of disease biomarkers, which are crucial in identifying diseases at their earliest stages and monitoring their progression. Machine learning models can detect subtle patterns in molecular data, helping to identify specific biomarkers associated with various diseases, including cancer, Alzheimer's, and cardiovascular conditions. These biomarkers serve as early warning signs and can guide clinicians in making timely and informed decisions about patient care.
Software dominates the market
Software in the context of AI encompasses a wide array of tools, platforms, and applications specifically designed to process, analyze, and interpret the immense volume of data generated in life sciences research. These software solutions utilize machine learning algorithms, natural language processing, deep learning, and other AI techniques to sift through complex biological datasets, making sense of genomics, proteomics, and clinical data. The versatility of AI software allows researchers to explore various aspects of drug discovery, disease diagnosis, and patient care with unprecedented precision and efficiency. Additionally, the scalability and adaptability of AI software make it a preferred choice for organizations operating in the life sciences domain. Researchers can customize and fine-tune AI algorithms to meet their specific research needs, whether it involves drug target identification, biomarker discovery, or patient stratification for clinical trials. This flexibility empowers scientists to adapt to evolving research objectives and swiftly respond to emerging challenges in healthcare and life sciences. Furthermore, AI software offerings are at the forefront of addressing some of the most pressing issues in the industry.
Cloud-based dominate the market
Cloud-based deployment offers unparalleled scalability and flexibility, which are crucial for the resource-intensive nature of AI applications in life sciences. Researchers and organizations can tap into cloud resources as needed, scaling up or down depending on the complexity and volume of data being processed. This dynamic scalability ensures that computational resources are optimally allocated, avoiding underutilization or resource bottlenecks, which can occur with on-premises solutions. Additionally, cloud-based deployment eliminates the need for significant upfront hardware and infrastructure investments. This cost-effectiveness is particularly attractive for research institutions, pharmaceutical companies, and healthcare providers looking to leverage AI without the burden of substantial capital expenditures. Cloud services provide pay-as-you-go pricing models, allowing organizations to pay only for the computing resources they consume, thus optimizing cost management. Moreover, cloud-based deployments offer the advantage of accessibility and collaboration. Researchers and scientists can access AI tools and applications from anywhere with an internet connection, facilitating collaboration across geographic boundaries and enabling real-time data sharing and analysis.
Drug discovery dominates the market
AI-driven drug discovery is not limited to target identification alone. AI models can predict the pharmacokinetics and toxicity profiles of potential drugs, allowing researchers to assess their safety and efficacy earlier in the development pipeline. This risk mitigation not only saves time but also reduces the likelihood of costly late-stage failures, a common challenge in the pharmaceutical industry. Additionally, AI plays a pivotal role in drug repurposing, where existing drugs are explored for new therapeutic applications. By analyzing biological data, AI algorithms can identify overlooked connections between drugs and diseases, potentially unveiling novel treatment options. This approach not only accelerates the availability of treatments for various medical conditions but also leverages existing knowledge and resources more efficiently. Furthermore, the personalized medicine revolution is closely linked to AI-driven drug discovery. As AI models analyze patients' genetic profiles, clinical histories, and real-time health data, they can identify specific genetic markers and mutations that influence drug response.
North America exhibits a clear dominance, accounting for the largest artificial intelligence in life sciences market share
The market research report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America accounted for the largest market share.
North America boasts significant investments in AI research and development. Government initiatives, private sector funding, and venture capital investments have poured into AI projects and startups, fueling innovation and technological advancements. This financial backing has accelerated the growth of AI-driven solutions, from drug discovery and genomics to healthcare analytics and personalized medicine. Moreover, North America's robust regulatory framework and intellectual property protection create a conducive environment for AI development and commercialization. Several regulatory agencies have been proactive in engaging with AI developers to establish clear guidelines and approval processes for AI-based medical devices and treatments. This regulatory clarity gives businesses confidence to invest in AI projects. Furthermore, North America's healthcare infrastructure is among the most advanced globally, making it a prime testing ground for AI applications. The region's large patient population, extensive electronic health record systems, and well-established pharmaceutical and biotech industries provide ample opportunities for AI-driven healthcare solutions to demonstrate their efficacy and impact.
Numerous companies in this market are focused on using AI to accelerate drug discovery processes. They develop AI algorithms and platforms that analyze biological data, identify potential drug candidates, predict drug interactions, and optimize drug design, all with the goal of bringing new therapies to market faster and more efficiently. Also, AI companies in the life sciences sector work on solutions for genomic analysis. They develop tools that can decipher and interpret genetic information, identify disease markers, predict disease risk, and enable personalized medicine by tailoring treatments based on an individual's genetic profile. Moreover, companies are developing AI-driven solutions that assist radiologists and pathologists in interpreting medical images such as X-rays, MRIs, and CT scans. These tools can help detect diseases and anomalies earlier and with greater accuracy. Companies are also actively engaged in predictive analytics, utilizing AI to identify disease biomarkers, predict patient outcomes, and stratify patients for clinical trials. These AI-driven insights can inform treatment decisions and improve patient care.
Table 7 Global: Artificial Intelligence In Life Sciences Market: Key Players