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There is no evidence that AI misdiagnosis is bankrupting hospitals. Diagnostic errors are a serious patient-safety and liability problem, and clinical AI can affect clinicians’ decisions. But the large published cost estimates concern diagnostic errors generally—not errors caused by AI—and studies of AI safety reports do not establish how often AI causes harm or what it costs hospitals.
How common are diagnostic errors in hospitals?
Diagnostic error is an established healthcare safety issue, but the available figures measure different things and should not be treated as one universal rate. The Agency for Healthcare Research and Quality (AHRQ) says diagnostic errors contribute to about 10% of patient deaths and are a primary reason for medical liability claims. That statement concerns diagnostic errors broadly; it does not attribute those deaths or claims to AI.
AHRQ’s 2025 final report on the UPSIDE project gives a more specific, but deliberately limited, picture. Reviewers found a diagnostic error in 550 of 2,428 records (23.0%; 95% confidence interval 20.9–25.3%). The records came from 29 hospitals and were selected from patients who died in hospital or transferred to an ICU. This high-risk cohort is not representative of all hospital admissions, so 23% is not an estimate of the error rate for every hospitalized patient.
In 436 of the same 2,428 records (17.8%), reviewers judged that diagnostic error contributed to temporary harm, permanent harm, or death. This is a finding about the selected records, not a rate of AI-related harm.
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What do the financial estimates actually measure?
Published estimates show that diagnostic errors can carry substantial financial consequences. They are not estimates of losses caused by clinical AI, and they use different methods and time periods. They should not be added together or described as a current annual bill for hospitals.
| Estimate | What it measures | How to interpret it |
|---|---|---|
| $5.7 billion over 12 years | AHRQ’s current-state diagnostic safety issue brief summarizes this estimate for inpatient diagnostic errors generally. | It is an estimate for diagnostic errors, not AI-attributable losses or an annual total. |
| $5.7 billion in payments over the study period | A 2017 hospital malpractice-claims study reported inpatient diagnosis-related paid claims totaling this amount. | Paid claims are not all costs of error, all hospital expenses, or a current annual total. Claims data capture only a selected set of cases. |
| $100 billion annually in the United States | The OECD’s 2025 report discusses a cited estimate of diagnostic-error costs that includes malpractice litigation costs. | The OECD notes that estimates depend on definitions, care settings, and how errors are detected and reported. It is not an audited total or an AI-specific estimate. |
These figures answer different questions: one estimates a broader economic burden, another totals payments on a particular set of malpractice claims, and the OECD discusses a much larger annual estimate with methodological caveats. The AHRQ issue brief describes the evidence base for quantifying diagnostic-error costs as limited. A claims study also found that paid diagnosis-related claims were associated with greater adjusted risks of death and disability than other paid claim types, but that comparison does not show what AI caused or what a particular hospital will lose.
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Can AI make diagnostic errors worse?
Yes, it can influence decisions in ways that worsen accuracy, although the available evidence does not show that deployed AI is increasing hospital misdiagnosis rates overall. In a 2023 randomized clinical-vignette study summarized by AHRQ PSNet, hospital clinicians assessed cases involving acute respiratory failure from pneumonia, heart failure, or COPD. Baseline clinician diagnostic accuracy was 73%. AI suggestions improved accuracy overall in the study, but systematically biased model predictions had a larger adverse effect on clinicians’ accuracy.
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What do FDA device safety reports tell us?
FDA adverse-event reports can help identify possible safety signals, but they cannot by themselves tell hospitals the incidence of harm or prove that a device caused a reported event. They are voluntary reports without a complete denominator, and reporting may be incomplete or uneven.
- A 2024 study summarized by AHRQ PSNet in 2025 identified 429 FDA MAUDE device safety reports associated with AI/ML-enabled devices; about one-quarter were potentially related to AI/ML. This is a count of reports, not a risk rate.
- An earlier analysis by Lyell and colleagues found that mammography was implicated in 69% of the reports they examined, with most events described as near misses. That percentage applies only to that reported-event dataset, not to all AI devices or clinical use.
Near misses and reports warrant review, but neither these findings nor the vignette study quantify AI-caused misdiagnoses, hospital-wide incidence, or financial exposure.
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Why can a clinical AI tool’s performance change after deployment?
A model is used within a changing clinical environment. FDA research on postmarket monitoring notes that data-acquisition systems, clinical protocols, and patient populations can change. Inputs may also differ from the data a system was designed or evaluated to handle; such out-of-distribution inputs can produce unexpected results. A tool’s performance at one site or time therefore does not automatically establish its performance across other sites or later periods.
FDA describes postmarket work aimed at detecting changes in AI-device inputs, monitoring output performance, and understanding causes of performance variation. These are quality-assurance approaches, not guarantees that a system will be safe or produce a financial return.
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How should hospitals evaluate and monitor diagnostic AI?
Hospitals considering or already using a diagnostic AI tool need to treat it as part of a clinical workflow, not as a one-time software purchase. The evidence supports attention to intended use, validation, ongoing monitoring, auditability, and a response process if performance changes. It does not establish a single checklist that guarantees safe deployment.
- Define intended use and workflow. Specify the clinical task, who sees the output, when it is used, and how the output is meant to inform—not silently replace—clinical judgment.
- Check validation relevance. Consider whether evaluation included patient populations and clinical sites comparable to the hospital’s own, and whether performance has been assessed across multiple sites.
- Plan for out-of-distribution inputs. Identify how the system may behave when data, equipment, acquisition methods, or patient characteristics differ from the conditions it was evaluated on.
- Monitor inputs and outputs over time. Establish ways to detect input drift and changes in output performance rather than assuming that initial validation remains sufficient.
- Make outputs reviewable and auditable. Ensure the organization can examine outputs and evaluate performance variation as part of quality assurance.
- Set a response and reporting process. Decide who reviews a concerning change, how the tool’s use is reassessed, and how safety events and near misses are handled.
These measures support oversight; the available evidence does not show that they eliminate errors or prove that monitoring will reduce costs by a specific amount.
What the evidence supports—and what it does not
Diagnostic errors can harm patients and generate liability and care costs. AI can shape clinician decisions, and biased suggestions or changes in data and clinical context create credible safety concerns. But the evidence summarized here does not establish that AI misdiagnosis is bankrupting hospitals, nor does it provide a reliable total for AI-attributable hospital costs. The responsible conclusion is narrower: hospitals should monitor clinical AI as a changing component of care, while keeping general diagnostic-error estimates separate from AI-specific evidence.
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