The Artificial Intelligence (AI) in Diagnostics Market market is forecast to grow at a CAGR of 34.4%, reaching USD 13.6 billion in 2031 from USD 3.1 billion in 2026.
The Artificial Intelligence (AI) in diagnostics market is witnessing consistent growth due to the rising prevalence of chronic diseases, such as cancer, diabetes, cardiovascular disease, and neurological conditions, which is further boosted by the rise in aging populations and sedentary lifestyles. Furthermore, there is a rise in technological advancement and innovations for unprecedented accuracy, scalability, and speed in analysing medical data, which is also promoting the demand for integration of AI in diagnostics.
According to data from the World Health Organization (WHO), cancer cases are expected to grow by 77% from 2022 to 2050, i.e., from 20 million cases in 2022 to more than 35 million cases by 2050 globally. This is mainly driven by the rise in the aging population and behavioural risk factors like alcohol, tobacco, and obesity. The integration of AI in diagnostic analysis supports predictive strategies, timely intervention while optimizing healthcare resources in the growing burden of healthcare settings.
In addition to this, the global shortage in healthcare professionals, such as pathologists, diagnostics, and radiologists, along with an aging workforce and burnout among these healthcare professionals, also demands for AI enabled diagnostic systems.
Moreover, the AI-powered point-of-care (POC) diagnostic devices are evolving to enhance diagnostic accuracy, reduce interpretation errors, and facilitate rapid clinical decision-making. This offers a shift from traditional centralized laboratories to an accessible, automated, and rapid decentralized system, which can perform testing near patients in homes, emergency rooms, and remote healthcare settings.
For instance, in June 2025, Philips announced the global launch of its POC ultrasound system, i.e., Flash Ultrasound System 5100 POC, for supporting the critical care, aesthetic needs, emergency medicine requirement along with musculoskeletal imaging. This portable system is developed with smart automation and intuitive touch screen controls to offer mobility, precision, and speed to support real-time decision making in critical environments like ICUs, trauma units, and emergency rooms.
Increasing Prevalence of Chronic Diseases
The rising global prevalence of chronic health conditions is the main driving factor behind the increasing adoption of AI technologies within the diagnostic market. The aging population, together with inactive lifestyles, urban development, and genetic factors, has caused an increase in cases of cancer, cardiovascular diseases, diabetes, and neurological disorders, which include Alzheimer's and Parkinson's disease and chronic kidney disease.
Cardiovascular disease persists as the primary cause of disease burden according to the Global Burden of Disease (GBD), which reports that one-third of all global deaths result from this condition. The Oxford Academic report in EAPC showed that cardiovascular disease will rise by 90 percent from 2025 to 2050, while crude mortality rates will increase by 73.4 percent and crude disability-adjusted life years will rise by 54.7 percent in the same period. In addition, it is predicted that the cardiovascular disease-related deaths will rise from 20.5 million in 2025 to 35.6 million in 2050. The surge of new patients exceeds the standard diagnostic systems' capacity, which leads to an increase in market demand as AI delivers accurate diagnostic solutions that need to scale up their operations.
The AI system addresses the challenge by enabling early disease identification and customized risk evaluation. It uses its advanced capabilities to analyze extensive data collections through its pattern recognition system. The AI algorithms use medical imaging data and patient records to detect small health changes that may indicate potential early-stage cancer and cardiovascular disease indicators, with better precision than human diagnosticians.
Furthermore, AI models in neurology use multiple data sources to predict dementia progression based on EEG signals, genetic information, and cognitive assessment results. For instance, the University of Nebraska Medical Center AI tool achieved this through MRI scans and Alzheimer's detection record analysis. The AI system enables healthcare professionals to implement timely medical solutions while distributing their resources across multiple medical facilities, resulting in high patient demand.
Advancements in Machine Learning and Deep Learning Algorithms have enhanced the AI in Diagnostics Market Growth.
Machine learning and deep learning algorithms have significantly contributed to the expansion of AI in the diagnostics business. These algorithms have shown an extraordinary ability to handle and analyze massive volumes of medical data, allowing for precise and rapid diagnosis. According to a report published by the WHO, a deep learning system detected skin cancer with an accuracy of 94.5%, outperforming human dermatologists. Furthermore, according to a study published in the Journal of the American Medical Association[1], an AI system properly recognized breast cancer in mammograms with a sensitivity of 94.5%. These developments demonstrate the power of machine learning and deep learning algorithms in improving diagnostic accuracy and patient outcomes.
AI in Diagnostics Market: Integration of AI into Medical Imaging Technologies.
Medical imaging integrated with AI has brought several changes in the diagnostics industry. Several pieces of literature support the idea that the application of AI algorithms can improve the quality and efficiency of medical picture processing. A recent article in Nature shows evidence that an AI system outperforms radiologists at diagnosing lung cancer from CT images with 97% sensitivity. Another test in The Lancet Oncology reported that an AI system correctly diagnosed breast cancer on mammograms with 90.2% sensitivity. These studies are examples of the capability to improve diagnosis by integrating AI and medical imagery technology.
Improvement in Data Security and Privacy Measures Strengthens AI in Diagnostics Market.
Improvements in data security and privacy policies have facilitated AI use in the diagnostics sector. Data breaches and privacy issues have been among the major impediments to AI use in health. On the other hand, advances in encryption, anonymization methods, and secure data transport protocols have addressed these issues. Data security and privacy advancements have instilled trust in patients and healthcare professionals, allowing for the broad use of AI in diagnostics.
Enhanced Processing Power and Storage Capabilities in AI in Diagnostics Market.
AI in diagnostics has grown dramatically as processor power and storage capacity have improved. Because of the exponential development in processing power and the availability of large-scale storage options, massive volumes of medical data may now be analyzed in real-time. For example, research published in the Journal of the American Medical Association demonstrated that AI algorithms with an area under the receiver operating characteristic curve (AUC-ROC) of 0.936 may reliably detect diabetic retinopathy by analyzing retinal pictures. These processing power and storage advances have revolutionized diagnostic speed and efficiency, allowing for rapid and precise medical judgments
2026: 20/20 BioLabs Launches OneTest for Longevity Blood Test and Chronic Disease Risk Assessment and Management Solution Built with IBM AI Capabilities.
In September 2025, Aidoc received a U.S. Food & Drug Administration (FDA) Breakthrough Device Designation for its multi-triage AI solution built on its CARE™ foundation model, which flags a wide range of life-threatening, time-sensitive conditions across CT scans in a single workflow.
In July 2025, GE HealthCare stated it had topped the FDA’s list of AI-enabled medical device authorisations in the U.S. and underscored its increased R&D investment in AI-enabled devices (software and smart systems) across diagnostics, smart imaging, and clinical workflows.
In March 2025, GE HealthCare and NVIDIA announced a collaboration to advance autonomous diagnostic imaging (X-ray and ultrasound) using NVIDIA’s Isaac for Healthcare physical-AI simulation platform to help automate workflows and expand access.
In September 2024, Roche introduced new AI-driven cancer diagnostics by expanding its digital pathology open environment. The company's digital pathology environment will bridge many innovative AI-based pathology labs to help clinics improve patient care and extend personalized healthcare. It has integrated more than 20 advanced artificial algorithms from eight new collaborations. By applying AI technology, this, in turn, can support scientists and pathologists in cancer diagnostics and cancer research.
In May 2024, GE HealthCare launched a new generation of radiation therapy computer tomography solutions, including novel software and hardware solutions. These solutions will contribute to increasing image accuracy while simplifying stimulation for personalized and seamless oncology pathways for both patients and health professionals.
Based on diagnostic type, the artificial intelligence (AI) in the diagnostics market is divided into radiology, pathology, cardiology, oncology, neurology, and others. With the booming technological adoption in medical applications, investments in next-generation approaches such as Artificial Intelligence (AI), which leverages machine learning (ML) and deep learning (DL) models to improve the overall medical data analysis process, are gaining traction. Likewise, the growing prevalence of chronic diseases globally has become a major health concern, which has propelled the demand for new innovations that increase the efficiency of early disease detection.
According to the World Bank studies, cancer is one of the major causes of death globally, with lung, breast, prostate, colon, and rectum being the most common types. Hence, to reduce the complexities in medical diagnosis of such health concerns and prevent delays in providing accurate treatment, AI-based diagnosis has provided a way forward by using multi-modal data to identify patterns that offer more precise disease detection.
Various research studies are being conducted to explore the usage of AI tools for cancer diagnostics. For instance, the “Harnessing AI for Cancer Care in Europe” report issued by the European Cancer Organization in November 2025 outlines how AI adoption can accelerate earlier cancer detection and diagnostic precision.
Additionally, various strategic collaborations followed by investments in initiatives are being witnessed to integrate AI in analyzing real-time data for providing necessary insight regarding disease progression. For instance, in October 2025, the European Commission launched the “COMPASS-AI” initiative, which aimed to drive AI adoption among clinical applications, including disease detection for major issues like cancer.
Major regional economies like the USA, which is witnessing a high cancer prevalence rate, are emphasizing investing to improve cancer diagnostics, which has played a major role in driving development in AI-solutions to address such health concerns. According to the American Cancer Society, in 2025, the estimated number of new cancer cases in the USA stood at 1,053,250 among males and 988,660 among females.
Based on diagnostic settings, the artificial intelligence (AI) in the diagnostics market is divided into central laboratory diagnostics, point-of-care diagnostics (POC), and home diagnostics. The evolving healthcare needs, followed by challenges in accessing treatment, have significantly increased the demand for innovative diagnostic tools that support on-site medical testing based on real-time data. This shift is increasing the transition towards modern technological concepts such as Artificial Intelligence (AI) for point-of-care diagnostics.
Moreover, the global surge in infectious diseases further necessitates frequent and accessible monitoring for immediate results and effective management outside laboratories. AI-based tools fulfil such requirements, and to further boost their adoption in POC, various collaborations and product development are currently underway.
For instance, in January 2026, digital health innovator Qure.ai announced that the company received a multi-million-dollar grant from Gates Foundation to develop AI-enabled point-of-care ultrasound algorithm that will use in the early detection of major infectious disease like pneumonia and tuberculosis (TB) both of which jointly causes nearly 3.23 million deaths annually (1.23 million deaths by Tuberculosis, 2 million deaths by pneumonia).
Similarly, to reduce the duration of timely response, various AI platforms are investing in improving their model algorithm. For instance, in May 2025, Ambient Healthcare launched its new advanced AI platform, powered by OpenAI’s “Reinforcement Fine-Tuning (RFT) Technology”, which amplifies its accuracy in ICD-10 coding by 27%. The platform address coding at POC eliminates downstream compliance issues.
Various projects in joint public-private collaboration are also being implemented to improve AI-based told adoption in point of care, for instance, in August 2025, the Indian Government along with Technology Development Board (TBD) and Department of Science and Technology formed an agreement with M/s Primary Healthtech Pvt. Ltd. for “IoT-enabled Point-of-care Blood Testing Device for Affordable and Accessible Healthcare powered by AI/ML algorithms” project, that aims to increase efficiency of Mobilab (M1) prototype in performing five test simultaneously, thereby reducing patient’s waiting time.
In the United States, the use of Artificial Intelligence (AI) in the diagnostic market is growing exponentially, driven by a robust innovation ecosystem supported by substantial investments and regulatory advancements.
Several factors are driving market growth, such as technological advancements and growth in machine learning and deep learning, which allow AI systems to better analyse a large variety of datasets with unprecedented precision, resulting in the reduction of errors by up to 30% in pathology and radiology. The explosion of healthcare data, fueled by EHR proliferation and wearable devices, provides fertile ground for AI training, while demand for personalized medicine tailors diagnostics to individual profiles, accelerating adoption in oncology and cardiology.
Economic and operational pressures are also propelling market growth. Due to ongoing nationwide labor shortages caused by an aging population and post-pandemic burnout, healthcare providers are increasingly turning to automation for routine tasks. They are utilizing AI triage tools to prioritize urgent cases and help reduce radiologist workload.
IBM Corporation
General Electric (GE) Company
Siemens Healthineers AG
Aidoc Medical Ltd.
Zebra Medical Vision Ltd.
Butterfly Network, Inc.
Viz.Ai, Inc.
Imagen Technologies, Inc.
Alivecor, Inc.
PathAI Inc.
IBM Corporation, a pioneer in computing since 1911, entered the AI diagnostic market through its groundbreaking work in cognitive computing, aiming to revolutionize healthcare by harnessing artificial intelligence to interpret complex medical data. The company's foray began with the development of Watson, an AI system inspired by the Jeopardy! -winning computer, which was adapted to process unstructured medical information like patient records, imaging scans, and research papers.
This marked IBM's shift from general AI research rooted in decades of advancements in machine learning and natural language processing to targeted applications in diagnostics, where AI could assist clinicians in pattern recognition and decision-making.
| Report Metric | Details |
|---|---|
| Total Market Size in 2026 | USD 3.1 billion |
| Total Market Size in 2031 | USD 13.6 billion |
| Forecast Unit | Billion |
| Growth Rate | 34.4% |
| Study Period | 2021 to 2031 |
| Historical Data | 2021 to 2024 |
| Base Year | 2025 |
| Forecast Period | 2026 – 2031 |
| Segmentation | Component, Diagnostic Type, Diagnostic Settings, Geography |
| Geographical Segmentation | North America, South America, Europe, Middle East and Africa, Asia Pacific |
| Companies |
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