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AI diagnostic and decision-support systems could help extend clinical capacity where staff or resources are scarce, but their use does not automatically make care more equitable. Their effects depend on the data and prior practices they inherit, who can access them, how clinicians use their outputs, and whether their performance is monitored and challenged.
Can AI diagnostics improve healthcare equity?
Potentially—but potential is not proof of an equity benefit. The World Health Organization (WHO) identifies workforce gaps and resource limitations as challenges AI may help address. That could matter where access to clinical expertise is constrained. But the cited WHO overview does not quantify improved diagnostic outcomes or show that any gains are distributed fairly.
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Equity depends on more than whether a tool exists. A system could extend support to some settings while remaining unavailable, unaffordable, or poorly suited to others. WHO’s Director-General, Tedros Adhanom Ghebreyesus, has said: “The future of healthcare is digital, and we must do what we can to promote universal access to these innovations and prevent them from becoming another driver for inequity.”
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There is also a difference between AI used to support clinical decisions and a specific diagnostic product validated for a defined task. General guidance about AI’s promise or risks does not establish that a particular system is accurate, safe, or fair for a particular patient group.
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How can AI systems reproduce health disparities?
Unrepresentative or incomplete data
A model’s behavior can reflect the information used to develop it. WHO warns that biased or insufficiently broad data may exclude disadvantaged groups. If some populations, clinical settings, or conditions are poorly represented, a system may be less useful for them even when it performs acceptably in other contexts.
Patterns in existing care
Data can also encode disparities in prior healthcare. A model trained on records shaped by unequal access or treatment may reproduce those patterns in its predictions or recommendations. Adding AI to a workflow does not, by itself, correct the conditions that produced the underlying data.
Different impacts across groups
Fairness cannot be inferred from an overall performance claim alone. Relevant questions include which groups were represented in validation, what errors occurred for each group, and what those errors mean in the intended clinical setting. The evidence summarized here does not establish comparative diagnostic performance or measured equity effects across demographic groups.
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What can go wrong when clinicians rely on AI output?
WHO’s guidance on large multimodal models (LMMs)—generative systems that accept multiple types of data—warns that outputs may be false, inaccurate, biased, or incomplete. It also identifies automation bias: people may defer to a system’s output instead of questioning it. In healthcare, that can make errors harder to catch if a recommendation is treated as authoritative rather than as information to assess.
These warnings are specific to risks WHO identifies for LMMs; they are not measured error rates for every diagnostic AI system. The practical lesson is to understand what a tool is intended to do, what it cannot establish, and how its output fits into a clinician’s judgment and the wider care process.
WHO also flags cybersecurity risks involving patient information and trustworthiness of algorithms. Protecting health data and maintaining confidence in a system are therefore part of responsible deployment, not separate from it.
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What should clinicians and health systems check before using a tool?
WHO’s governance recommendations emphasize transparency, accountability, human supervision, inclusive access, and monitoring for disproportionate effects. For a specific system, those principles translate into concrete questions:
- Purpose: What clinical task is the system intended to support, and in which setting? Evidence for one task or workflow should not be treated as proof for another.
- Validation: Which patients and settings were represented when the system was evaluated? Are results available for relevant subgroups, as well as overall?
- Errors and consequences: What kinds of mistakes can occur, and what might follow from a false positive, a missed finding, or an incomplete recommendation?
- Human oversight: Who reviews the output, when can they disregard or question it, and what happens when the AI and clinician disagree?
- Transparency and updates: Can clinical users understand the system’s intended use and limitations? How are changes monitored and communicated?
- Access and infrastructure: Which patients and care settings can realistically use the system, considering affordability, accessibility, and technical requirements?
- Privacy and security: How is patient information protected, and how are security concerns handled?
- Monitoring and redress: Can patients or clinicians raise concerns, seek review, and obtain a remedy? Are outcomes monitored for disproportionate effects?
WHO recommends involving stakeholders in system design and, for large-scale deployment, using independent post-release audits and impact assessments, with outcomes disaggregated by user group. These are WHO recommendations, not universal statutory requirements. Monitoring matters because a tool’s effects in actual use may differ from what was expected during development.
What do U.S. and international rules cover?
Different policy frameworks apply to different kinds of systems. Certified health IT algorithms, medical devices, and generative LMMs should not be treated as if they fall under one interchangeable regulatory regime.
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ONC’s HTI-1 rule: predictive algorithms in certified health IT
The U.S. Office of the National Coordinator for Health Information Technology (ONC) says its HTI-1 Final Rule establishes transparency requirements for AI and other predictive algorithms that are part of certified health IT. The information is intended to help clinical users assess fairness, appropriateness, validity, effectiveness, and safety. ONC says the rule’s provisions took effect on March 11, 2024; this scope does not mean every healthcare AI product is covered.
ONC also reports that more than 96% of U.S. hospitals and 78% of U.S. office-based physicians are supported by ONC-certified health IT. Those figures describe the reach of certified health IT, not AI adoption and not evidence that AI has improved care or equity.
FDA and international partners: machine-learning-enabled medical devices
On June 13, 2024, the U.S. Food and Drug Administration (FDA) reported that FDA, Health Canada, and the U.K.’s Medicines and Healthcare products Regulatory Agency had jointly published guiding principles for transparency of machine-learning-enabled medical devices. FDA emphasizes that communication should reflect a device’s context of use and intended audience.
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FDA’s January 2025 announcement: draft guidance
On January 6, 2025, FDA announced draft lifecycle recommendations for AI-enabled medical devices, including transparency and bias strategies, and requested public comments by April 7, 2025. That announcement described draft guidance, not a final rule. The subsequent status of the draft is not established here, so its recommendations should not be presented as current binding requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does the evidence establish—and what remains unknown?
The available evidence supports a careful conclusion: AI may help address capacity constraints, and recognized risks include biased or incomplete data, errors, automation bias, unequal access, and privacy or security concerns. Governance recommendations and transparency policies offer ways to assess and manage those risks.
It does not establish that a particular diagnostic AI system improves accuracy, reduces disparities, or produces a quantified equity benefit. Those claims require evidence about a defined product, task, population, and setting. Until such evidence is available, neither broad promises of fairer care nor broad claims that AI inevitably worsens equity are justified.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsIn its January 18, 2024 news release, WHO Chief Scientist Dr. Jeremy Farrar put the condition plainly: “Generative AI technologies have the potential to improve health care but only if those who develop, regulate, and use these technologies identify and fully account for the associated risks.”
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