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Doctors can use AI to support clinical and public-health work, but no tool is trustworthy simply because it sounds confident, reports high accuracy, or is labelled “AI.” Its value and risks depend on the specific task, patient population, clinical workflow, and safeguards. Before adopting one, clinicians and health-care organizations need evidence for that intended use, protections for patients, meaningful human oversight, and a plan to monitor what happens after deployment.
What does AI in health actually include?
AI in health is not one technology or a single kind of clinical decision support. The World Health Organization (WHO) describes uses spanning diagnosis and screening, clinical care, research and drug development, public-health surveillance, outbreak response, and health-system management. Evidence that one system performs well for one task does not establish that another system is safe or effective for a different task or population.
A useful first distinction is what the system does with its input. Conventional predictive AI generally estimates or classifies something from data; generative AI produces new text or other content; and large multimodal models (LMMs) can accept one or more types of input and generate outputs that need not be the same type. These categories can overlap in products, so the label alone does not tell a doctor what a system is validated to do.
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|---|---|---|
| Predictive AI | Estimate a risk or classify an input for a defined task. | Whether the model was evaluated for the specific decision, population, and workflow where it will be used. |
| Generative AI, including large language models (LLMs) | Produce text such as summaries, explanations, or answers to prompts. | Whether outputs are accurate for the intended task, how errors are caught, and whether patient information entered into the application is protected. |
| Large multimodal models (LMMs) | Accept one or more types of input and generate diverse output types. | Whether the claimed capability has been demonstrated for the particular clinical use; broad general-purpose capability is not proof of clinical effectiveness. |
WHO’s 2025 guidance on LMMs discusses predicted applications in health care, research, public health, and drug development while noting that broad general-purpose capability has not yet been proven. A possible application should therefore be treated as a proposal to evaluate, not as evidence of a clinical benefit.
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Can doctors trust an AI diagnosis or answer?
Trust should be earned for a particular use, not transferred from a technology category, product name, or impressive demonstration. An output may be useful as an aid to a clinician’s reasoning, but it is not a diagnosis simply because it is plausible or delivered with confidence. WHO’s 2023 warning about LLMs emphasizes that their answers can sound authoritative while being completely incorrect or seriously erroneous. Training-data bias, consent concerns, sensitive information supplied to applications, and generated disinformation are also among the risks it identifies.
For any proposed clinical use, ask for evidence that matches the decision being made and the people and workflow involved. A general accuracy claim is not enough to establish that a tool will perform appropriately in a particular service, country, or patient group. WHO calls for clear evidence of benefit before LLMs are used widely in routine health care and medicine; its guidance is not product-specific validation for any named system.
How should doctors judge whether an AI tool is ethical?
WHO’s six connected principles offer a practical framework for examining a proposed tool. They apply across design and deployment, not just at procurement.
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Protect autonomy
People should remain in control of health systems and medical decisions. Consider whether patients can understand the tool’s role in their care, whether they can make informed choices where appropriate, and how privacy, confidentiality, and valid consent are protected. A tool should not quietly turn a supportive recommendation into an unchallengeable decision.
Promote well-being, safety, and the public interest
Define the intended use before judging performance: what decision or task is supported, for whom, and in which setting? Then assess whether evidence and applicable safety, accuracy, and efficacy requirements support that use. There should also be quality control and a way to improve the system in practice. Adoption should address a real need rather than divert attention from core health-system investments.
Ensure transparency and intelligibility
Clinicians and affected people need enough information to scrutinize how a system is designed and deployed and to understand its role in a decision. Ask what documentation is available, what the output means, and what it cannot establish. Transparency does not require every user to be able to inspect a model’s internal mathematics; it does require that the system’s purpose, limitations, and place in the workflow are clear enough for meaningful oversight and discussion.
Foster responsibility and accountability
Responsibility does not disappear when a model contributes to a decision. Establish who reviews outputs, who may override them, who reports errors, and who acts to correct problems. People affected by a decision should have a route to question it and seek redress. WHO’s guidance emphasizes that stakeholders remain responsible for appropriate conditions and trained use.
Ensure inclusion and equity
Ask which groups were represented or excluded in design and evaluation, and whether performance differences are measured. Relevant differences can include age, sex, gender, income, race, ethnicity, sexual orientation, and ability. WHO cautions that systems trained mainly on high-income-country data may not perform well in low- and middle-income settings. Equity is therefore a question of both measured performance and who has a voice in design, access, and oversight.
Promote responsiveness and sustainability
Evaluation should continue during actual use, not end at launch. Monitor how the tool responds to its real setting and users, look for unintended effects, and consider environmental consequences. If the people or conditions a system serves change, reassess whether its role remains appropriate.
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What should a clinic or health system check before adoption?
Use a defined review rather than relying on a vendor demonstration or a single headline metric. The following questions translate WHO’s principles and cautions into an adoption check:
- Need and intended use: What specific clinical or public-health need does the tool address? Which users, patients, decisions, and settings are in scope?
- Relevant evidence: Has the tool been evaluated for that task, population, and workflow? What does the evidence establish, and what remains untested?
- Uncertainty and errors: How does it signal uncertainty? What kinds of mistakes are foreseeable, and what happens when its output is incomplete, wrong, or unavailable?
- Representation and equity: Which populations were included or excluded in evaluation? How will differences in performance be detected and addressed?
- Privacy and data governance: What patient or operational data are collected, where are they sent, how are they retained, and who can access them? What protections and permissions apply?
- Workflow and oversight: Who reviews the output, when can it be overridden, and how will staff be trained to use it appropriately? Can patients and clinicians tell what role it played?
- Accountability and follow-up: Who receives reports of errors, who can correct a problem, and how will performance, unintended effects, and subgroup disparities be monitored after deployment?
Record the answers and make the tool’s role explicit in local procedures. Requirements differ by jurisdiction, product, and use; organizations should check applicable law, regulation, and institutional policy rather than treating general ethical guidance as legal advice.
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Can I put patient information into an AI chatbot?
Do not enter identifiable or otherwise sensitive patient information into a chatbot unless your organization has authorized that specific service and confirmed that its data handling meets applicable privacy, security, consent, and institutional requirements. WHO warns that sensitive information supplied to LLM applications can be put at risk and raises concerns about consent. A chatbot’s convenience or public availability does not establish that it is approved for clinical information.
If a service has been approved for a defined purpose, follow the organization’s rules about what information may be entered and how outputs must be checked. Do not use generated text as a substitute for verifying clinical facts or for the required review of a patient-care decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who is responsible if an AI tool makes a mistake?
There is no basis for assuming that responsibility belongs to the software alone or that a clinician is automatically responsible for every system failure. Accountability depends on the circumstances, roles, and applicable rules. As an ethical and operational matter, the organization should define responsibilities before use: who selects and configures the tool, who trains users, who reviews its output, who can override a recommendation, and who investigates and corrects reported errors. Patients need a way to question decisions and seek redress.
Clinicians should use a tool only within its defined role and should not treat its output as self-validating. That does not replace the organization’s responsibility to provide safe conditions, appropriate training, and a process for handling failures. The exact legal duties depend on jurisdiction and context.
What does AI mean for health research oversight?
AI creates ethics questions not only when used in clinical care but also when health research uses AI or studies AI itself. WHO’s report Artificial intelligence-related health research: ethics review and oversight, published 21 July 2026, covers health-related data science using AI, research conducted with AI tools and technologies, and research on AI tools and technologies. It identifies challenges for research ethics committees and gaps in existing oversight, including concerns about fairness, benefit sharing, power imbalances, and capacity building, with particular attention to low- and middle-income countries.
For researchers and clinicians serving on review committees, the implication is to examine who benefits, who bears risk, whose data and priorities shape the work, and whether local oversight has the capacity to assess the proposed methods. The use of an AI tool does not remove the need for appropriate research ethics review.
How should doctors approach AI now?
WHO’s guidance supports considering AI for a defined health need, while rejecting the idea that enthusiasm or technical novelty is evidence of benefit. In 2021, WHO Director-General Dr Tedros Adhanom Ghebreyesus said: “Like all new technology, artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology it can also be misused and cause harm.” For doctors, the practical response is to ask what the tool is for, what evidence supports that use, how patients are protected, and who remains accountable—then keep evaluating it in practice.
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