AI in Healthcare: How It Is Used in Patient Care Today

AI in healthcare is used to read medical images, draft clinical documentation, flag patients at risk of deterioration and answer patients’ questions. The strongest clinical evidence so far is in imaging, and most tools support clinicians rather than replace them.

How widely is AI used?

Use among doctors has grown fast. In the American Medical Association’s 2026 survey of nearly 1,700 physicians, 81% said they use AI professionally, more than double the share in 2023. The most common uses were administrative rather than diagnostic:

• Summarising medical research and standards of care (39%)
• Drafting discharge instructions, care plans or progress notes (30%)
• Documenting billing codes, medical charts or visit notes (28%)
• Generating chart summaries (28%)

What AI does in healthcare

In practice, healthcare AI uses machine learning and data analysis to analyse records, images and test results, support diagnosis, estimate the risk of future outcomes and take over routine administrative work. The final clinical decision stays with the clinician.

Main applications in patient care

1. Medical imaging and early detection

Imaging is the most established area. Radiology accounts for by far the largest share of entries on the FDA’s list of AI-enabled medical devices authorized for marketing in the United States.

The best-known trial is Sweden’s MASAI study of more than 100,000 women, with full results published in The Lancet in January 2026. AI-supported mammography detected 81% of cancers at screening, compared with 74% for standard double reading, and led to 12% fewer interval cancers (cancers found between screenings). False-positive rates were similar in both groups. Earlier interim results showed a 44% lower screen-reading workload for radiologists. At least one radiologist still read every exam.

2. Clinical documentation and administration

Drafting notes, discharge instructions, billing codes and chart summaries is where physicians use AI most, as the AMA figures above show. The aim is to reduce paperwork and give clinicians more time with patients.

3. Predictive analytics

Models trained on patient records can flag people at higher risk of deterioration, complications or readmission, so care teams can step in earlier. Their value depends on how well they are validated on the hospital’s own patient population.

4. Personalised treatment

AI can combine genetic data, medical history and lifestyle information to help clinicians choose treatments suited to an individual patient, particularly in areas such as oncology.

5. Remote patient monitoring

Wearables and connected devices track heart rate, blood pressure and activity between visits. AI helps sort the incoming data so that clinicians see alerts that need action, which can reduce unnecessary hospital visits.

6. Patient-facing assistants

Chatbots answer questions, help with scheduling and explain instructions around the clock. Demand is high: OpenAI reported in January 2026 that more than 40 million people ask ChatGPT health questions every day, and about 7 in 10 of those conversations happen outside normal clinic hours. General-purpose chatbots are not medical devices, so their answers still need checking with a clinician.

7. Drug discovery

Pharmaceutical researchers use machine learning to screen candidate molecules and analyse trial data, with the goal of shortening early research stages.

Challenges

• Privacy and security: patient data is sensitive and regulated. In the AMA survey, 86% of physicians stressed the importance of data privacy.
• Validation and regulation: 88% of physicians in the same survey want robust evidence of safety and effectiveness. Clinical tools may need regulatory clearance before use.
• Skills: 88% of physicians were concerned about trainees losing skills if they rely on AI.
• Integration: tools must fit into existing record systems and clinical workflows.
• Governance of generative AI: the World Health Organization has published guidance on the ethics and governance of large multi-modal models for governments, developers and health providers.

What comes next

The likely direction is closer cooperation between AI and clinicians rather than automation of clinical judgement: more continuous monitoring through wearables, wider use of genetic data in treatment decisions, and more virtual consultations supported by AI triage and documentation. The MASAI design, where AI supports but does not replace the radiologist, is a good model for how these tools are being introduced.

What healthcare organisations should check before adopting AI

• Start with administrative tasks such as documentation, where the risk to patients is lower.
• For clinical tools, check regulatory status (for example, FDA authorization in the US).
• Ask for evidence that the tool works on a patient population like yours.
• Keep a clinician responsible for every clinical decision.
• Confirm how the vendor stores and processes patient data.