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AI diagnostic systems could help extend clinical capacity where staff or resources are limited, but that possibility is not proof of better or fairer care. Their effects depend on whose data shaped them, how they fit into clinical workflows, who can access them, and whether people can detect and challenge harmful decisions.
Can AI diagnostics make healthcare more equitable?
Potentially—but not automatically. The World Health Organization (WHO) identifies workforce gaps and resource limitations as challenges that AI may help address. A tool that supports clinical work could be useful in settings where specialists or services are scarce. But the WHO overview does not quantify improved diagnostic outcomes or show that any benefits are shared equitably.
That distinction matters: expanding a tool’s availability is not the same as improving health outcomes, and improving an average outcome would not establish that underserved groups benefit too. The evidence summarized here establishes plausible benefits, recognized risks, and policy safeguards; it does not establish that a particular diagnostic AI system improves accuracy, reduces disparities, or produces a measured equity effect.
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How can AI reproduce or deepen disparities?
Data can leave groups out
A model learns patterns from the data used to develop it. If those data do not adequately represent the people who will use the system, its performance may not carry over to those groups. WHO warns that biased or insufficiently broad data can exclude disadvantaged populations. It also identifies race, ethnicity, ancestry, sex, gender identity, and age among dimensions where bias in large multimodal models (LMMs) may arise.
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An LMM accepts multiple types of input, but WHO’s warnings about LMMs should not be treated as measured error rates for every kind of diagnostic AI. The broader lesson is that developers and health systems need evidence about the intended population and task, rather than assuming a model works equally well for everyone.
Past care can become a model’s template
Data can also reflect disparities in existing care. If some communities have historically had less access to diagnosis or treatment, records may capture those differences. A system trained on such records can reproduce patterns of care without establishing that they are medically appropriate. WHO warns that disparities in existing care may be carried into AI systems.
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Workflow can turn a suggestion into a decision
A clinician may give an AI output more weight than it deserves, especially when the system appears confident or is embedded in a familiar workflow. WHO calls this automation bias and warns that LMMs can produce false, inaccurate, biased, or incomplete statements. These are recognized risk pathways, not evidence of a quantified failure rate across diagnostic tools.
Who benefits depends on access as well as accuracy
A system can be technically capable yet unavailable to people who need it. WHO flags affordability and accessibility concerns for the best-performing LMMs, and its Director-General has called for universal access so that AI does not become another driver of inequity. Access can depend on whether a health service can afford the system and has the infrastructure and staff to use it; the sources summarized here do not establish comparative access levels for particular products or communities.
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WHO also identifies cybersecurity risks involving patient information and algorithm trustworthiness. Protecting health data and maintaining confidence in how a system operates are part of responsible deployment, not separate from the equity question.
What safeguards can make deployment more responsible?
Check the evidence for the people and task involved
Before relying on an AI tool, a health system should be able to explain what clinical task it is intended to support and what evidence applies to the patients who will encounter it. Relevant questions include:
- Which clinical task and care setting is the system intended for?
- What population was included in its validation, and which relevant groups were represented?
- What kinds of errors can occur, and what would those errors mean for patients?
- How will the tool fit into clinical work, and who is responsible for reviewing its output?
- How are changes to the system communicated and monitored?
- What accessibility, affordability, infrastructure, privacy, and security needs affect its use?
These questions are a practical way to examine whether evidence and deployment conditions fit the intended use; they are not a claim that every product has the same regulatory obligations.
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WHO’s broader AI governance principles call for transparency and intelligibility, responsibility and accountability, human supervision, and mechanisms for questioning and redress. In practice, a clinical team needs to understand what a system is meant to do, how its output should inform care, and how patients or clinicians can raise concerns. Human involvement is meaningful only when people can review the output and have the authority and information needed to respond to it.
Monitor effects after deployment
Performance in development does not guarantee that a tool will behave the same way in every setting or over time. WHO recommends monitoring for disproportionate effects and, for LMMs deployed at large scale, stakeholder participation in design as well as independent post-release audits and impact assessments with outcomes disaggregated by user group. These are WHO recommendations, not universal statutory requirements.
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| Policy or guidance | Scope described by its source | What it means for readers |
|---|---|---|
| ONC HTI-1 Final Rule | Transparency requirements for AI and other predictive algorithms that are part of certified health IT. ONC says the information is intended to help clinical users assess fairness, appropriateness, validity, effectiveness, and safety. The provisions took effect on March 11, 2024. | It concerns algorithms in certified health IT, not every healthcare AI product. ONC reported that more than 96% of U.S. hospitals and 78% of U.S. office-based physicians were supported by ONC-certified health IT. Those figures describe the reach of certified health IT; they are not AI adoption rates or evidence of improved care or equity. |
| FDA, Health Canada, and U.K. MHRA transparency principles | The agencies jointly published guiding principles for transparency of machine-learning-enabled medical devices in June 2024. FDA says communication should account for the device’s context of use and intended audience. | These principles concern medical-device transparency; they are distinct from ONC’s certified-health-IT requirements. |
| FDA draft guidance announcement | FDA’s January 6, 2025 announcement described draft lifecycle recommendations for AI-enabled medical devices, including transparency and bias strategies, and requested comments by April 7, 2025. | The announcement described draft guidance, not a final rule. It should not be treated as the same policy as HTI-1 or as a universal requirement for all healthcare AI. |
These policies address different categories of technology. WHO’s 2024 guidance on LMMs informs the broader discussion of generative systems that accept multiple data types; it does not establish the performance of a particular diagnostic product. HHS’s 2025 AI Strategic Plan likewise frames AI as a tool to support existing efforts, not a sole solution to underlying problems. It notes that AI can misclassify needs, harm health outcomes, or increase costs, and highlights equitable access and appropriate human oversight.
What to conclude from the evidence
AI may help extend clinical capacity, but healthcare equity depends on more than the existence of a tool. Evidence about the intended task and population, access to the system, accountable human oversight, and monitoring for unequal effects all shape whether its potential benefits are shared. WHO Chief Scientist Dr. Jeremy Farrar summarized the condition in a January 18, 2024 news release: “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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