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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesDoctors should treat health AI as a set of task-specific tools to evaluate—not as a single technology to trust or reject. Before using one, establish what it is meant to do, whether it has evidence for the intended patients and workflow, how its risks will be controlled, and who will review and act on its output. A fluent answer or general accuracy claim is not enough.
What “AI in health” includes—and what it does not prove
Artificial intelligence in health spans different tasks, systems, and levels of consequence. The World Health Organization (WHO) identifies applications including diagnosis and screening, clinical care, research and drug development, public-health surveillance, outbreak response, and health-system management. Those categories are not evidence that any particular product works safely for a particular use. Performance for one task, model, population, or setting does not establish performance for another.
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Predictive tools
Conventional predictive AI uses data to estimate or classify an outcome—for example, identifying a pattern associated with a clinical risk. Its output is an estimate tied to the data, target, and setting for which the system was developed and evaluated. A result should not be treated as a diagnosis or recommendation beyond that defined use.
Generative and multimodal tools
Generative AI, including large language models (LLMs), produces text or other content in response to prompts. WHO warned in May 2023 that an LLM can produce answers that sound authoritative and plausible while being completely incorrect or seriously erroneous. It also highlighted bias in training data, consent concerns, risks to sensitive information submitted to applications, and the potential to generate convincing disinformation.
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WHO’s March 2025 guidance describes large multimodal models (LMMs) as systems that can accept one or more types of input and produce diverse outputs that are not limited to the input type. The guidance discusses predicted applications in health care, research, public health, and drug development; it also says broad general-purpose capability has not yet been proven. A proposed application or impressive demonstration is not the same as demonstrated clinical effectiveness.
Can doctors trust an AI diagnosis?
Not on the basis of fluency, a product demonstration, or a headline accuracy figure alone. The relevant question is whether the specific system has credible evidence for its intended task, patients, and real clinical workflow—and whether people can detect and manage its failures. WHO’s 2023 LLM guidance calls for clear evidence of benefit before widespread routine use in health care and medicine.
Ask for evidence that matches the decision the tool will influence. A model evaluated on one population or workflow may not perform the same way in another. Find out how it handles uncertain or incomplete inputs, which groups were represented or excluded from evaluation, and whether performance is monitored across relevant patient groups. Consider what happens when an output is wrong, unavailable, or inconsistent with the clinical picture.
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AI can inform professional judgment; it does not remove the need to assess the patient, consider other evidence, and decide whether the output is appropriate. WHO’s 2021 guidance frames health AI around people’s well-being, safety, autonomy, and public interest rather than technology adoption for its own sake.
Six ethical questions to apply before adopting a tool
WHO’s 2021 guidance sets out six connected principles. They can be used as a practical review framework by clinicians and health-care organizations.
1. Does it protect patient autonomy?
Patients should remain in control of health systems and medical decisions. Consider whether people are told when AI is involved, whether they can ask questions or decline its use where appropriate, and whether the tool affects privacy, confidentiality, or valid informed consent. Keep the system’s role understandable to the patient and care team.
2. Is there a defined safety case and meaningful benefit?
Specify the intended use, the clinical need it addresses, and the population for whom it is meant. Ask what safety, accuracy, and efficacy requirements apply, what quality controls are in place, and how the tool will be improved or withdrawn if problems emerge. Do not assume that a tool’s general usefulness establishes benefit in your setting.
3. Can its design and use be scrutinized?
Transparency does not necessarily mean that every user can inspect a model’s internal workings. It does mean that enough information about design and deployment should be documented and accessible for meaningful scrutiny and public discussion. Ask what the system is intended to do, what evidence supports it, what its known limits are, and how its output should be interpreted.
4. Are responsibility and routes for redress clear?
Agree in advance who checks outputs, who can override them, who acts on an alert, and how a suspected error is reported, investigated, and corrected. Staff need appropriate training and conditions for safe use. Patients and other affected people should have a way to question decisions and seek redress. A vendor’s involvement does not by itself answer how responsibilities are allocated in a particular organization or jurisdiction.
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5. Is performance and access equitable?
Equity is both a performance concern and a governance responsibility. WHO has cautioned that systems trained mainly on data from high-income countries may not perform well in low- and middle-income settings. Examine which groups and settings were represented or excluded, and how differences in performance will be detected and addressed. WHO’s guidance calls for attention to differences including age, sex, gender, income, race, ethnicity, sexual orientation, and ability.
6. Will the system be responsive and sustainable in practice?
Plan to evaluate the tool during actual use, not just before launch. Check whether it remains responsive to the needs of patients and staff, whether its effects change over time, and what environmental consequences are relevant. Establish who reviews monitoring results and what conditions would trigger a change, pause, or removal.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Can you put patient information into an AI chatbot?
Do not enter identifiable or otherwise sensitive patient information into a general-purpose chatbot unless your organization has confirmed that the specific application, configuration, and use are authorized and meet applicable privacy, confidentiality, security, and consent requirements. WHO’s May 2023 warning specifically raises risks associated with sensitive information supplied to applications and with consent. A system being easy to access does not establish that it is approved for clinical data.
Before any use involving patient information, confirm what data are collected, where they are processed, how long they are retained, who can access them, and whether they may be used for other purposes. Follow institutional policies and applicable local law; requirements differ by jurisdiction and service. If those conditions are not established, do not submit patient data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical adoption and monitoring checklist
Use this sequence before introducing an AI tool into clinical or public-health work:
- Define the job. Write down the intended task, the decision it may influence, the users, the patient population, and the setting. Identify the clinical need and the alternative to using the tool.
- Review relevant evidence. Request validation for the actual task, population, and workflow—not a broad accuracy claim. Check how uncertainty and errors are handled and what limitations are known.
- Check equity and data governance. Ask which groups were represented or excluded, how subgroup performance will be assessed, what information the tool collects and retains, and how sensitive data are protected.
- Set human oversight and accountability. Name who reviews outputs, who has authority to override them, how staff are trained, and how affected people can raise concerns. Define how errors are reported and corrected.
- Evaluate the live use. Monitor performance, unintended effects, and disparities after deployment. Specify who reviews results and what findings would require correction, a pause, or withdrawal.
These checks reflect WHO’s ethical principles and its cautions about LLMs; they are not a substitute for checking local laws, regulations, and institutional policies. The evidence and governance requirements should be considered for each specific tool and use, rather than inferred from the category of AI.
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AI raises ethical questions in health research, too
AI can be the subject of health research, a tool used to conduct research, or part of data science using AI. WHO’s report Artificial intelligence-related health research: ethics review and oversight, published 21 July 2026, addresses all three: 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 clinicians involved in research, ethical review should account for the role AI actually plays, the data and people affected, and who benefits from the work. The existence of a model or a research proposal does not settle those questions.
What doctors should take away
AI may support clinical and public-health work, but its value depends on a defined use, evidence relevant to that use, safeguards for patients, and continuing oversight. Generative output can be convincingly wrong; performance can vary across populations and settings; and privacy, transparency, equity, and accountability need active attention. The right decision is not whether to trust “AI” in general, but whether a particular tool is justified and safe in the particular work it is being asked to do.
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