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市場調查報告書
商品編碼
1358975
到 2030 年醫療診斷中的人工智慧 (AI) 市場預測:依組件、領域、模式、AI 技術、用途、最終用戶和地區進行的全球分析Artificial Intelligence in Medical Diagnostics Market Forecasts to 2030 - Global Analysis By Component, Specialty, Modality, AI Technology, Application, End User and By Geography |
根據 Stratistics MRC 預測,2023 年全球醫療診斷領域人工智慧 (AI) 市場規模將達到 13 億美元,預計到 2030 年將達到 105 億美元,預測期內年複合成長率為 34.2%。 。
醫療診斷中的人工智慧 (AI) 可以幫助醫療保健專業人員為患者做出準確、及時的治療決策,從而有可能改善護理的可及性並降低護理成本。Masu。準確診斷疾病需要多年的醫學教育和大量的時間。人工智慧在醫療診斷中的應用已經證明能夠提供準確的診斷、支持臨床判斷、提高醫療保健專業人員的判斷力。
電子健康記錄(EHR)、醫學影像資料和基因組資料等大量資料的現成可用,使得開發和檢驗人工智慧模型成為可能。此外,醫療保健資料的數位化和可互操作系統的引入使得收集和使用這些資料變得更加容易,使人工智慧演算法能夠從不同的患者群體中學習並提高診斷準確性。
醫療保健公司面臨的主要障礙是資金籌措,特別是在開發中國家,很難將 IT 資金優先於醫療設備。影像設備的高成本以及人工智慧軟體的實施和許可成本是限制市場成長的主要問題,特別是在醫療報銷條件較差的國家。然而,開發中國家的大多數醫療機構無力實施人工智慧解決方案,例如由於安裝和維護成本高昂。這一要素阻礙了創新和尖端系統的引入。
由於行業數位化和資訊系統的引入,巨量資料(龐大而複雜的資料)在醫療保健提供過程的各個階段產生。醫療診斷領域的巨量資料包括點擊流、網路和社交媒體互動產生的資訊、感測器、心電圖、X光等醫療設備的讀數以及其他申請記錄、生物識別資料等。包括。此外,近年來,巨量資料和分析解決方案變得更加複雜和廣泛使用,醫療保健相關人員越來越接受電子病歷、數位化檢查幻燈片和高解析度放射影像。
深度學習模型,尤其是人工智慧演算法中使用的模型,可能很複雜且難以理解。醫護人員可能會發現很難信任和理解人工智慧產生的診斷背後的邏輯,因為目前尚不清楚人工智慧如何得出結論。然而,為了讓人工智慧模型被醫療保健專業人員接受和認可,它們必須易於存取和檢測。
COVID-19 的疫情對全球醫療保健產業產生了負面影響。 COVID-19 感染率急劇上升,對全球衛生系統帶來巨大壓力。 COVID-19 患者通常會出現肺部問題。因此,乳房攝影篩檢已成為確定COVID-19患者嚴重程度的標準診斷程序。 2020 年,使用 AI 診斷 COVID-19 的研究迅速增加。
診斷領域對基於人工智慧的軟體及時提供準確診斷的需求不斷增加,新的人工智慧演算法快速開發並核准新軟體,放射科、循環系統、神經科、婦科、眼科等軟體領域佔據最大佔有率預測期內由於基於人工智慧的軟體在各領域的應用。儘管面臨人員短缺和影像掃描量增加的課題,軟體解決方案也為醫療保健提供者提供了相對於競爭對手的競爭優勢。
由於實施基於人工智慧的解決方案可實現自動化診斷和減少醫院負擔的要素、接受診斷程序的患者數量不斷增加、對疾病早期發現的需求不斷成長以及專業專家的短缺等因素,醫院部門預計在整個預測期內見證盈利成長。此外,醫院對用於診斷的基於人工智慧的醫療技術的需求日益成長,以減少複雜性和錯誤,節省金錢和時間,並由專業人員和技術純熟勞工快速輕鬆地執行。許多醫院正在與數位公司合作,為患者提供雲端基礎的人工智慧服務和解決方案。透過在日常業務中利用這些解決方案,醫院可以提高生產力和效率。
由於各種慢性病和感染疾病的罹患率不斷上升、主要在中國和印度的人工智慧新興企業數量不斷增加,以及人工智慧填補人工智慧領域空白的巨大潛力,亞太地區將在預測期內持續成長。該地區的醫療基礎設施預計將佔據最大的市場佔有率。此外,股權投資和新興企業的孵化也影響著區域市場的發展。該地區人口高齡化的加劇以及急性和慢性疾病患病的增加預計將支持該地區的市場擴張。
由於對準確、快速診斷的需求不斷增加以及世界高齡化導致慢性病發病率上升等要素,亞太地區有望盈利成長。其他好處包括幫助放射科醫生解讀醫學影像以做出快速準確的診斷、減少醫學影像中的雜訊以及以較低劑量的輻射生成高品質影像。例如,
According to Stratistics MRC, the Global Artificial Intelligence (AI) in Medical Diagnostics Market is accounted for $1.3 billion in 2023 and is expected to reach $10.5 billion by 2030 growing at a CAGR of 34.2% during the forecast period. By supporting healthcare professionals in making accurate and timely treatment decisions for their patients, artificial intelligence (AI) in medical diagnostics has the potential to improve access to and the cost of healthcare. It takes years of medical education and a lot of time to diagnose a condition accurately. The application of AI to medical diagnosis has demonstrated its ability to provide precise diagnoses, support clinical decisions, and improve healthcare professionals judgment.
According to the data by the World Bank, USD 1,111.082 was spent per capita on healthcare in 2018.
Electronic health records (EHRs), medical imaging data, and genomic data, which have become readily available in huge amounts, have made it possible to develop and validate AI models. Moreover, the collection and use of these data have been made easier by the digitization of healthcare data and the deployment of interoperable systems, enabling AI algorithms to learn from a variety of patient groups and increase diagnostic precision.
The main obstacle facing healthcare companies is funding, particularly in developing nations where it is difficult to prioritize IT funds over medical equipment. Particularly in nations where the reimbursement situation is unfavorable, the high cost of imaging equipment and the implementation and licensing expenses of AI software are the main issues limiting market growth. However, due to high installation and maintenance costs, for instance, the majority of healthcare facilities in developing nations cannot afford AI solutions. The adoption of innovative or cutting-edge systems is being hampered by this factor.
Big data (huge and complex data) is produced at various phases of the care delivery process as a result of the industry's growing digitization and adoption of information systems. Big data in the field of medical diagnostics includes, among other things, information generated from clickstream and web and social media interactions, readings from medical devices like sensors, ECGs, X-rays, and other billing records, as well as biometric data. Additionally, with the increasing acceptance of EHRs, digitized laboratory slides, and high-resolution radiological images among medical professionals over the past few years, big data and analytical solutions have become exponentially more advanced and widely used.
Deep learning models in particular, which are used in AI algorithms, can be complex and challenging to understand. Healthcare practitioners might discover it difficult to trust and comprehend the logic behind AI-generated diagnoses due to the ambiguity of how AI comes to its conclusions. However, AI models must be accessible and measurable in order to be accepted and recognized by healthcare professionals.
The COVID-19 pandemic epidemic had a negative impact on the worldwide healthcare industry. The COVID-19 infection rate increased dramatically, placing an enormous burden on the global health system. Patients with COVID-19 typically experience lung problems. Therefore, to determine the severity of the disease in COVID-19 instances, cardiothoracic imaging is a standard diagnostic procedure. In 2020, the number of studies utilizing AI to diagnose COVID-19 rapidly increased.
Due to the rising demand for AI-based software in diagnostics to provide an accurate diagnosis in a timely manner, the rapid development of new AI algorithms and new software approvals, and the applications of AI-based software in a variety of fields, including radiology, cardiology, neurology, gynecology, and ophthalmology, among others, the software segment held the largest share over the projection period. Additionally, despite the challenges of having a shortage of employees and the need to deal with rising imaging scan volumes, software solutions give healthcare providers a competitive edge over their rivals.
Due to factors like the benefits of implementing AI-based solutions to automate diagnosis and reduce workload in hospitals, the rise in the number of patients undergoing diagnostic procedures, the expanding demand for early disease detection, and the shortage of medical specialists, the hospital segment is predicted to experience profitable growth throughout the forecast period. Furthermore, there is a growing need for AI-based medical technologies in hospitals that are used for diagnosis in order to reduce complexity and errors, save money and time, and be performed quickly and easily by professionals and skilled workers. Many hospitals have partnerships with digital firms to offer cloud-based AI services and solutions to their patients. By using these solutions in their daily operations, the hospitals will increase their productivity and efficiency.
Owing to the rising incidence of various chronic and infectious diseases, the rising number of AI-based startups, particularly in China and India, and the enormous potential of AI in filling the gap in the region's healthcare infrastructure, Asia Pacific is predicted to hold the largest share over the extrapolated period. Moreover, the availability of equity investments and start-up incubation has an impact on the development of regional markets. The region's rising aging population and higher prevalence of acute and chronic illnesses are both expected to boost market expansion in the region.
Due to factors including the increasing demand for accurate and prompt diagnosis and the rising frequency of chronic diseases owing to the aging population worldwide, Asia-Pacific is expected to have profitable growth. Additionally, the benefits offered by AI-based solutions in the diagnosis of different neurological diseases, such as helping radiologists interpret medical images to make a rapid and precise diagnosis, reducing noise in medical images, and producing high-quality images from lower doses of radiation, are enhancing regional growth.
Some of the key players in Artificial Intelligence (AI) in Medical Diagnostics market include: Orthofix Medical Inc., NuVasive, Inc., Baxter International Inc, OrthoPediatrics Corp., Arthrex, Inc, AlloSource, Wright Medical Group N.V., Stryker Corporation, GreenBone Ortho, Zimmer Biomet Holdings, Inc, Smith & Nephew Plc, GRAFTYS, Medtronic Plc, Bioventus Inc, Musculoskeletal Transplant Foundation, SeaSpine, GreenBone Ortho.
In September 2023, IBM commits to train 2 million in artificial intelligence in three years, with a Focus on Underrepresented Communities. To achieve this goal at a global scale, IBM is expanding AI education collaborations with universities globally, collaborating with partners to deliver AI training to adult learners, and launching new generative AI coursework through IBM SkillsBuild. This will expand upon IBM's existing programs and career-building platforms to offer enhanced access to AI education and in-demand technical roles.
In September 2023, IBM is offering a robust FSMA 204 traceability and compliance management solution capable of supporting the needs of the industry's largest enterprises and suppliers of all sizes. The solution combines the scalability and interoperability of the IBM Sterling Supply Chain Intelligence Suite and the IBM Food Trust Network with iFoodDS' traceability applications and innovative food industry, regulatory, and technical expertise.
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.