市場調查報告書
商品編碼
1466133
醫療診斷市場中的人工智慧:按組件、技術、應用和最終用戶分類 - 2024-2030 年全球預測Artificial Intelligence in Medical Diagnostics Market by Component (Hardware, Services, Software), Technology (Computer Vision, Machine Learning Platforms, Natural Language Processing), Application, End-User - Global Forecast 2024-2030 |
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預計2023年人工智慧醫療診斷市場規模為10億美元,2024年將達11.6億美元,2030年將達30.4億美元,複合年成長率為17.23%。
醫療診斷市場中的人工智慧(AI)包括基於人工智慧的技術和系統的開發、實施和實施,以分析臨床資料、識別模式並獲得見解,以提高診斷準確性和患者照護。慢性病盛行率的不斷上升急劇增加了診斷應用中增強影像分析的需求。越來越多的政府措施促進人工智慧/機器學習技術融入精準醫療和穿戴式設備,這正在加強產品開發,並為市場成長做出重大貢獻。然而,產品缺陷的增加以及人工智慧與現有診斷系統整合的困難可能會限制人工智慧診斷解決方案的市場採用。資料隱私和安全漏洞問題已成為市場成長的一個令人擔憂的因素。此外,用於醫療診斷的診斷機器人和先進人工智慧技術的引入正在為市場成長創造有吸引力的機會。隨著Start-Ups生態系統的發展和智慧醫院的擴張,人工智慧技術在醫療診斷中的應用預計將推動市場成長。
主要市場統計 | |
---|---|
基準年[2023] | 10億美元 |
預測年份 [2024] | 11.6億美元 |
預測年份 [2030] | 30.4億美元 |
複合年成長率(%) | 17.23% |
提供多種軟體元件以增強組件診斷決策
硬體是AI在醫療診斷領域的關鍵組成部分,是指AI運算所需的嵌入式系統、感測器、醫學影像處理設備等實體設備。感測器和物聯網設備是用於收集患者資料並將其發送到人工智慧系統進行分析的主要硬體。硬體環境需要硬體進行資料加密、存取控制以及遵守資料保護條例。服務包括人工智慧醫療診斷的培訓、維護、安裝和客製化,並利用該技術提供遠端監控和遠端諮詢。遠端監測包括遠距離診斷和持續監測患者的健康狀況,對於慢性疾病患者、老年人和偏遠地區的人們尤其有利。遠端醫療諮詢使專家醫療諮詢的普及不受地域限制,主要用於農村地區的後續觀察、初步診斷和醫療保健。該軟體是醫療診斷中人工智慧不可或缺的一部分,利用先進的演算法、機器學習和深度學習模型來分析複雜的醫療資料。它有助於解釋掃描影像、識別異常並預測患者預後和治療反應。軟體包括影像分析工具、診斷決策支援系統、基因組分析軟體、病理學和顯微鏡分析等。
技術:電腦視覺技術的重大進步改善了影像分析
電腦視覺涉及訓練人工智慧 (AI) 來解釋和理解視覺世界。在醫學診斷中,該技術使影像導引手術和放射學報告自動解讀等程序煥發活力。電腦視覺在放射學和病理學中極為重要,因為這些領域需要解釋大量影像資料。機器學習平台使電腦系統能夠隨著經驗的累積而改進,並能夠更好地預測疾病進展和早期診斷疾病狀況。該技術用於需要持續監測和及時干預的慢性疾病的診斷過程和管理,例如糖尿病和心臟病。自然語言處理(NLP)使人工智慧能夠理解和解釋人類語言。它對於簡化管理業務以及從醫療記錄中提取必要的資訊以進行患者照護非常有用。機器人流程自動化 (RPA) 是使用軟體機器人來自動執行日常任務,並且可以有效地自動化測試結果、更新患者記錄和自動化預約。 RPA 可以自動化大型醫院的整個檢測過程,消除錯誤並加快診斷速度。
應用:人工智慧在循環系統領域的應用,提高診斷準確性
人工智慧在循環系統醫學領域顯示出可喜的成果,包括心臟病的早期檢測和治療。使用人工智慧演算法,醫療專業人員可以根據患者的健康記錄和心臟影像來預測心臟麻痹、中風和心臟病的風險。它還成功地標記了心電圖 (ECG)資料中的異常情況,幫助醫生更準確地診斷節律性心臟疾病。神經系統疾病通常很複雜且難以診斷,人工智慧從大量資料中識別模式的能力使其受益匪淺。透過分析大腦影像掃描並識別人眼錯過的細微變化,人工智慧可以在老年失智症、帕金森氏症和多發性硬化症等疾病的早期檢測中發揮至關重要的作用。利用模式識別,人工智慧可以識別放射影像中可能預示癌症的異常情況,通常可以在腫瘤危及生命之前及早發現它們。人工智慧模型還可用於根據每種癌症的基因組成製定個人化治療計劃。隨著計算病理學的快速發展,人工智慧正在徹底改變病理學,因為人工智慧主導的演算法可以立即分析組織樣本並檢測異常、疾病和感染疾病。透過利用深度學習技術,人工智慧可以評估X光、 電腦斷層掃描和MRI掃描等醫學影像,以檢測肺炎、腦腫瘤和骨折等疾病的徵兆。在呼吸醫學領域,人工智慧用於預測和管理氣喘和慢性阻塞性肺病等慢性疾病,並透過分析電腦斷層掃描和解釋肺功能測試來幫助早期發現肺癌。在眼科領域,人工智慧演算法被用來診斷各種眼科疾病。深度學習模型可以分析視網膜照片以及早期發現糖尿病視網膜病變,顯著降低失明風險。
最終用戶:利用人工智慧在醫院和診所進行大規模資料集診斷
在學術機構和研究中心,人工智慧是探索和創新的核心。科學家和研究人員正在利用人工智慧設計早期疾病檢測的新方法,促進更快、更有效的診斷,進而實現及時介入。在診斷中心,人工智慧正在透過機器學習模型和影像識別軟體徹底改變患者照護,從而增強診斷成像。 AI 演算法可以分析 MRI 掃描、X 光掃描和電腦斷層掃描,以檢測異常情況並對其進行分類。這些工具有助於更準確的診斷,減少手動錯誤,並支持及時啟動適當的治療過程。醫療保健作為醫療體系第一線不可或缺的一部分,正在見證著人工智慧在多個方面的令人震驚的融合。主要是,它透過分析患者資料並向醫生即時提供重要見解來幫助醫生診斷疾病。透過採用人工智慧驅動的工具,醫院可以改善傳統的患者照護模式,加速診斷過程,並最終改善治療結果。
區域洞察
在美國,醫療保健人工智慧的大量投資正在帶來醫療診斷的突破性研究和進步。美國和加拿大等主要參與者擁有強大的影響力,並擁有透過人工智慧整合徹底改變醫療診斷服務的技術力。歐洲正在興起為醫療診斷研究和開發提供人工智慧驅動解決方案的新創公司和新興企業公司。全部區域政府、研究人員和產業參與者之間持續進行的合作活動在推動醫療診斷領域創新市場的成長方面發揮關鍵作用。亞太地區的主要國家,包括中國、日本和印度,正在幫助該地區的參與者利用其在機器人技術和先進技術方面的專業知識來開發人工智慧主導的診斷工具。由於人口眾多、醫療基礎設施不斷發展以及先進技術的採用增多,亞太地區醫療診斷領域的人工智慧 (AI) 正在經歷顯著成長。
FPNV定位矩陣
FPNV定位矩陣對於評估醫療診斷市場的人工智慧至關重要。我們檢視與業務策略和產品滿意度相關的關鍵指標,以對供應商進行全面評估。這種深入的分析使用戶能夠根據自己的要求做出明智的決策。根據評估,供應商被分為四個成功程度不同的像限:前沿(F)、探路者(P)、利基(N)和重要(V)。
市場佔有率分析
市場佔有率分析是一種綜合工具,可以對醫療診斷市場人工智慧供應商的現狀進行深入而詳細的研究。全面比較和分析供應商在整體收益、基本客群和其他關鍵指標方面的貢獻,以便更好地了解公司的績效及其在爭奪市場佔有率時面臨的挑戰。此外,該分析還提供了對該行業競爭特徵的寶貴見解,包括在研究基準年觀察到的累積、分散主導地位和合併特徵等因素。詳細程度的提高使供應商能夠做出更明智的決策並制定有效的策略,從而在市場上獲得競爭優勢。
1. 市場滲透率:提供有關主要企業所服務的市場的全面資訊。
2. 市場開拓:我們深入研究利潤豐厚的新興市場,並分析其在成熟細分市場的滲透率。
3. 市場多元化:提供有關新產品發布、開拓地區、最新發展和投資的詳細資訊。
4.競爭評估與資訊:對主要企業的市場佔有率、策略、產品、認證、監管狀況、專利狀況、製造能力等進行全面評估。
5. 產品開發與創新:提供對未來技術、研發活動和突破性產品開發的見解。
1.人工智慧在醫療診斷領域的市場規模及預測為何?
2.在醫療診斷市場人工智慧的預測期內,有哪些產品、細分市場、應用程式和領域需要考慮投資?
3. 醫療診斷市場人工智慧的技術趨勢和法規結構是什麼?
4.醫療診斷人工智慧市場主要廠商的市場佔有率是多少?
5. 進入人工智慧醫療診斷市場的合適型態和策略手段是什麼?
[189 Pages Report] The Artificial Intelligence in Medical Diagnostics Market size was estimated at USD 1.00 billion in 2023 and expected to reach USD 1.16 billion in 2024, at a CAGR 17.23% to reach USD 3.04 billion by 2030.
Artificial intelligence (AI) in the medical diagnostics market encompasses the development, implementation, and application of AI-based technologies and systems to analyze clinical data, identify patterns, and derive insights for improved diagnostic accuracy and patient care. The increasing prevalence of chronic disease conditions has surged the need for enhanced imaging analysis in diagnostic applications. Rising government initiatives to promote the integration of AI/ML technologies in precision medicine and wearable devices have enhanced product development, significantly contributing to market growth. However, increasing incidences of product failures and the difficulty of AI integration with existing diagnostic systems may limit the market adoption of AI-enabled diagnostic solutions. Data privacy and security breach issues have emerged as concerning factors for market growth. Moreover, the introduction of diagnostic robotics and advanced AI technologies for medical diagnosis has created attractive opportunities for market growth. The advancing start-up ecosystem and expansion of smart hospitals are expected to leverage AI technology in medical diagnostics to bolster the growth of the market.
KEY MARKET STATISTICS | |
---|---|
Base Year [2023] | USD 1.00 billion |
Estimated Year [2024] | USD 1.16 billion |
Forecast Year [2030] | USD 3.04 billion |
CAGR (%) | 17.23% |
Component: Availability of a diverse range of software components to offer enhanced diagnostics decision
Hardware is a key component of AI in medical diagnostics which refers to physical devices such as embedded systems, sensors, and medical imaging devices necessitated for AI computation. Sensors and IoT devices are major hardware used to collect patient data and transmit it to AI systems for analysis. Hardware environments require hardware for data encryption, access control, and compliance with data protection regulations. Services include training, maintenance, installation, and customization of AI medical diagnostics, which offer tele-monitoring and tele-consultation using the technology. Telemonitoring includes remote diagnostics and continuous monitoring of the patient's health, particularly beneficial for chronically ill patients, elderly people, and individuals residing in remote areas. Tele-consultation democratizes access to expert medical consultation irrespective of geographical barriers and is predominantly useful for follow-ups, preliminary diagnoses, and rural healthcare. Software forms an integral part of AI in medical diagnostics, leveraging sophisticated algorithms, machine learning, and deep learning models to analyze complex medical data. It helps to interpret scans, identify anomalies, and predict patient prognosis and treatment responses. This software may include image analysis tools, diagnostics decision support systems, genome analysis software, and pathology & microscopy analysis, among others.
Technology: Extensive advancements in computer vision technologies for improved image analysis
Computer vision involves training artificial intelligence (AI) to interpret and understand the visual world. In medical diagnostics, this technology has revitalized procedures such as image-guided surgeries and automated reading of radiology reports. Computer vision is crucial in radiology and pathology, where large volumes of image data are interpreted. Machine Learning platforms enable computer systems to improve with experience, and they excel in predicting disease progression and diagnosing conditions at early stages. This technology is used in the diagnosis process and management of chronic diseases such as diabetes or heart disease, which require continuous monitoring and timely interventions. Natural language processing (NLP) allows AI to understand and interpret human language. It is effective in streamlining administrative tasks and extracting essential information from medical records for patient care. Robotic process automation (RPA) is leveraging software robots to automate routine tasks and is efficient in automating laboratory results, updating patient records, and booking appointments. RPA can automate the entire laboratory process in large-scale hospitals, eliminating errors and speeding up diagnoses.
Application: Adoption of AI in cardiology segment to enhance diagnostic accuracy
Artificial intelligence has shown promising results in cardiology, including the early detection and treatment of heart diseases. Using AI algorithms, medical professionals can predict a patient's risk of cardiac arrest, strokes, and heart disease based on their health records and cardiac images. It has also been successful in flagging anomalies in electrocardiogram (ECG) data, aiding doctors in diagnosing rhythmic heart disorders more accurately. Neurological disorders, often complex and difficult to diagnose, significantly benefit from AI's capacity to recognize patterns in voluminous data. AI is pivotal in the early detection of conditions such as Alzheimer's, Parkinson's, and multiple sclerosis by analyzing brain imaging scans and identifying minute changes that the human eye may overlook. Using pattern recognition, AI can identify abnormalities in radiology images that can indicate cancer, often catching early-stage tumors before they become more life-threatening. AI models can also be utilized to formulate personalized treatment plans based on individual cancer genetic makeup. AI has revolutionized pathology by speeding up disease diagnostics with the surge of computational pathology, as AI-driven algorithms can instantaneously analyze tissue samples to detect abnormalities, diseases, and infections. Utilizing deep learning techniques, AI can evaluate medical images such as X-rays, CT scans, and MRI scans to detect signs of diseases, including pneumonia, brain tumors, and fractures. In pulmonology, AI is used to predict and manage chronic conditions such as asthma and COPD, and it helps with the early detection of lung cancer via the analysis of CT scans and interpretation of pulmonary function tests. Ophthalmology uses AI algorithms for diagnosing various eye diseases. Deep learning models can analyze retinal photos to detect diabetic retinopathy in its early stages, significantly reducing the risk of blindness.
End-User: Utilization of AI for large data set diagnosis in hospitals and clinics
Within academic institutions and research centers, AI is a focal point of exploration and innovation. Scientists and researchers leverage AI to devise new methodologies for early disease detection, facilitating faster and more efficient diagnosis and, in turn, enabling timely intervention. In diagnostic centers, AI is revolutionizing patient care with machine learning models and image recognition software, enabling enhanced diagnostic imaging. AI algorithms can analyze MRI scans, X-rays, and CT scans to detect and classify anomalies; this includes even minor abnormalities that can often escape unaided human interpretation. These tools facilitate more accurate diagnoses and reduce the scope of manual errors, supporting the timely beginning of an appropriate course of treatment. Hospitals, integral parts of the frontline healthcare system, are witnessing an impactful integration of AI in various capacities. Predominantly, it assists physicians in disease diagnosis by analyzing patient data and presenting key insights to the physician in real-time. By adopting AI-powered tools, hospitals can improve upon traditional patient care models, expedite the diagnosis process, and ultimately deliver improved treatment outcomes.
Regional Insights
Significant investments in AI for healthcare in the United States have led to groundbreaking research and advancements in medical diagnostics. Major countries such as the United States and Canada have the strong presence of key players equipped with technological capabilities to revolutionize medical diagnostics services through artificial intelligence integration. Europe has witnessed the emergence of several startups and established companies producing AI-driven solutions for research & development in medical diagnostics. Ongoing collaboration activities between governments, researchers, and industry players across the EMEA region are playing a crucial role to drive innovation market growth in the medical diagnostics sector. Significant countries in the APAC region, including China, Japan, and India, support regional players to leverage their expertise in robotics and advanced technologies to develop AI-driven diagnostic tools. Artificial Intelligence (AI) in medical diagnostics in the APAC has witnessed significant growth owing to its large population base, evolving healthcare infrastructure, and increasing adoption of advanced technologies.
FPNV Positioning Matrix
The FPNV Positioning Matrix is pivotal in evaluating the Artificial Intelligence in Medical Diagnostics Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).
Market Share Analysis
The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Artificial Intelligence in Medical Diagnostics Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.
Key Company Profiles
The report delves into recent significant developments in the Artificial Intelligence in Medical Diagnostics Market, highlighting leading vendors and their innovative profiles. These include 3M Company, AiCure, LLC, Aidoc Medical Ltd., Butterfly Network, Inc., Cera Care Limited, Cisco Systems, Inc., Corti - AI, Digital Diagnostics Inc., Edifecs, Inc., Enlitic, Inc., Epredia by PHC Holdings Corporation, Freenome Holdings, Inc., GE HealthCare Technologies, Inc., General Vision, Inc., Google LLC by Alphabet Inc., Hewlett Packard Enterprise Development LP, Imagen Technologies, Inc., Intel Corporation, International Business Machines Corporation, Johnson & Johnson Services, Inc., Kantify, Koninklijke Philips N.V., Medtronic PLC, Microsoft Corporation, Nano-X Imaging Ltd., NEC Corporation, NVIDIA Corporation, Persistent Systems Limited, Qure.ai Technologies Private limited, Siemens Healthineers AG, SigTuple Technologies Private Limited, Stryker Corporation, Tempus Labs, Inc., and VUNO Inc..
Market Segmentation & Coverage
1. Market Penetration: It presents comprehensive information on the market provided by key players.
2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.
3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.
4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.
5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.
1. What is the market size and forecast of the Artificial Intelligence in Medical Diagnostics Market?
2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Artificial Intelligence in Medical Diagnostics Market?
3. What are the technology trends and regulatory frameworks in the Artificial Intelligence in Medical Diagnostics Market?
4. What is the market share of the leading vendors in the Artificial Intelligence in Medical Diagnostics Market?
5. Which modes and strategic moves are suitable for entering the Artificial Intelligence in Medical Diagnostics Market?