Kings Research has released its landmark report on the global Artificial Intelligence in Healthcare Market, revealing that this critical sector is on a sustained high-growth trajectory with profound implications for global health outcomes and healthcare system sustainability. The market was valued at USD 10.34 billion in 2023, grew to USD 13.50 billion in 2024, and is forecast to reach USD 110.98 billion by 2031, exhibiting a CAGR of 35.12% throughout the forecast period. This growth trajectory mirrors the broader AI market’s expansion but is charged with particular urgency given the dual imperatives of improving patient outcomes and managing the rising costs of healthcare delivery in an aging global population.
Artificial intelligence in healthcare involves the application of advanced computational algorithms and machine learning techniques to analyze complex medical data, support disease diagnosis, personalize treatment protocols, predict health outcomes, and optimize operational processes across the care delivery continuum. From radiology AI that detects anomalies in medical images to natural language processing systems that extract clinical intelligence from unstructured physician notes, AI is being integrated across the full spectrum of healthcare functions with accelerating speed and increasing clinical evidence of efficacy.
Market Overview and Key Highlights
▶ Market valued at USD 10.34 billion in 2023, growing to USD 13.50 billion in 2024.
▶ Projected to reach USD 110.98 billion by 2031 at a CAGR of 35.12%.
▶ Key technology segments include machine learning, NLP, deep learning, and computer vision.
▶ Primary application areas: medical imaging and diagnostics, drug discovery, patient monitoring, and operational automation.
▶ Major players: IBM, Philips, NVIDIA, Medtronic, Siemens, Microsoft, Intel, GE, Pfizer, Eli Lilly, and others.
▶ Samsung launched its AI for All vision integrating AI with Samsung Health at CES 2025.
Medical Imaging and Diagnostics: The Flagship Application
Medical imaging and diagnostics represent the most commercially mature and clinically validated application area within the AI in healthcare market. AI algorithms trained on vast datasets of medical images — including X-rays, CT scans, MRI images, pathology slides, and fundus photographs — are demonstrating diagnostic accuracy that, in numerous peer-reviewed studies, matches or exceeds that of specialist physicians for specific conditions including cancers, diabetic retinopathy, cardiovascular disease, and pulmonary abnormalities.
The practical implications are substantial. AI diagnostic tools can process imaging studies in seconds, operate continuously without fatigue, maintain consistent analytical precision across thousands of cases, and flag anomalies that may be subtle enough to escape detection in a time-pressured clinical review. In underserved regions and healthcare systems facing specialist shortages, AI diagnostic tools represent a pathway to delivering specialist-level diagnostic capabilities without requiring specialist physician presence, potentially transforming access to quality diagnostic care globally.
Drug Discovery and Development: Compressing Timelines
Drug discovery and development is one of the most complex and costly endeavors in modern science, with an average timeline from initial target identification to regulatory approval historically spanning 10–15 years at a cost of USD 1–2 billion per approved drug. AI is dramatically compressing these timelines by accelerating multiple stages of the discovery and development process. Machine learning models can analyze vast libraries of molecular compounds to predict binding affinity, ADMET properties, and clinical efficacy, dramatically narrowing the candidate field for laboratory testing. AI systems can also identify novel drug targets by analyzing patterns in genomic, proteomic, and clinical datasets at a scale and speed impossible for human researchers.
Clinical trial design and patient recruitment — historically among the most expensive and time-consuming phases of drug development — are being optimized through AI-powered patient matching, real-world evidence analysis, and adaptive trial design. The COVID-19 pandemic demonstrated the potential of AI-accelerated drug development at unprecedented scale, and the pharmaceutical industry has substantially increased its investment in AI capabilities as a result. Major pharmaceutical companies including Pfizer, Eli Lilly, Roche, and Johnson & Johnson have all established significant internal AI research programs and formed partnerships with specialized AI drug discovery companies.
Patient Monitoring and Personalized Medicine
The proliferation of wearable health devices, remote patient monitoring systems, and electronic health record platforms is generating an unprecedented volume of longitudinal patient health data. AI systems that can continuously analyze these data streams — detecting early warning signs of deterioration, identifying patterns predictive of adverse events, and triggering timely clinical interventions — have the potential to shift healthcare from its traditionally reactive model to a proactive, preventive paradigm. This shift has profound implications for both patient outcomes and healthcare system costs, as early intervention is consistently more effective and less expensive than treating advanced disease.
Personalized medicine — the tailoring of treatment protocols to the individual patient’s genetic profile, disease characteristics, lifestyle factors, and treatment history — is another transformative application of AI in healthcare. By synthesizing multi-omic data with clinical records and real-world evidence, AI systems can identify patient subgroups most likely to respond to specific treatments, predict adverse reaction risks, and support oncologists in selecting optimal chemotherapy regimens for individual patients.
Operational Automation and Healthcare Administration
Beyond clinical applications, AI is delivering significant value in healthcare operations and administration. Natural language processing is automating clinical documentation, reducing the documentation burden on physicians and freeing time for patient care. AI-powered revenue cycle management systems are improving claims accuracy and reducing denials. Predictive scheduling algorithms are optimizing operating room utilization and staffing levels. Supply chain AI systems are reducing pharmaceutical and medical supply waste. Collectively, these operational applications represent a substantial and growing portion of the AI in healthcare market, particularly attractive to health systems focused on financial sustainability.
Competitive Landscape and Strategic Outlook
The AI in healthcare market features both established healthcare technology companies and technology sector entrants. Major players include IBM Corporation, Koninklijke Philips N.V., NVIDIA Corporation, Medtronic, Siemens AG, Microsoft Corporation, Intel Corporation, General Electric Company, Pfizer, Eli Lilly, Epic Systems Corporation, Oracle, Johnson & Johnson, Amazon, and Roche. Strategic partnerships between healthcare providers, health systems, technology companies, and pharmaceutical firms are proliferating as organizations seek to access AI capabilities at scale.
The Kings Research AI in Healthcare Market report is available at www.kingsresearch.com/artificial-intelligence-in-healthcare-market-1255.
About Kings Research
Kings Research is a leading global market research and consulting organization providing comprehensive industry analysis, competitive intelligence, and strategic advisory services across more than 50 verticals and 100+ countries. Our reports empower investors, enterprises, and governments with actionable, data-driven insights. For inquiries, visit www.kingsresearch.com.
