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Not for autonomous use, based on current evidence. General-purpose vision-language models (VLMs) can be useful assistants in tightly bounded, validated workflows, but published evaluations do not establish the reliability needed to make high-stakes medical or safety decisions on their own. In a 2025 neuroradiology study, the best evaluated model scored 35% accuracy versus 86.2% for neuroradiologists, with harmful outputs in as many as 45% of cases. In a separate safety-critical driving benchmark, none of 14 evaluated vision-language models exceeded 40% accuracy.
Those figures do not prove that every VLM fails every consequential task. They show why a general benchmark score cannot establish deployment readiness. Suitability depends on the exact decision, the consequences of each error, representative testing, subgroup and operating-condition performance, the human-AI workflow, and monitoring after deployment.
What “accurate enough” means in a high-stakes setting
For a high-stakes application, accuracy is not simply the percentage of test questions answered correctly. A model may achieve a respectable average while missing rare but dangerous findings, generating confident false alarms, or failing when information arrives in a different order. The relevant question is whether the complete system can keep risk within an acceptable limit for a defined use.
- Define the decision: Specify the action the model will support, such as flagging a scan for review, drafting a report, or selecting a driving response. “Understand images” is too vague to validate.
- Define the harm: A missed stroke, an unnecessary treatment delay, and a harmless formatting error do not have equal consequences. Weight errors by severity, not only by count.
- Define the operating envelope: Identify the populations, equipment, image quality, languages, lighting, weather, and information sources the system is expected to handle.
- Define the safety mechanism: Establish when the model must abstain, escalate to a qualified person, or stop being used.
A model can be accurate enough for a low-consequence drafting task while being unacceptable for diagnosis, triage, treatment selection, or control of a vehicle. The threshold is therefore use-specific rather than a universal percentage.
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What medical-imaging evaluations show
Neuroradiology: a large gap on 100 brain and spine cases
Meddeb and colleagues evaluated five commercial and open-source VLMs against neuroradiologists on 100 brain and spine cases from Radiopaedia. The neuroradiologists achieved a mean accuracy of 86.2%. Gemini 2.0, the best-performing model in that study, reached 35%. Asking models to provide their three leading differential diagnoses improved results only marginally.
The investigators reported harmful outputs in up to 45% of cases. Errors included incorrect anatomical localization, misinterpretation of images, hallucinated findings, and missed pathology. The potential consequences included delayed treatment. On those cases and methods, the authors concluded that the evaluated systems were not reliable enough for neuroradiological clinical decision support without expert oversight.
These are not universal scores for every medical-image model. The sample was 100 curated cases, the models were specific versions evaluated in a study published on 17 November 2025, and later systems or different tasks could perform differently. The result is still a clear warning against treating a general-purpose VLM as an autonomous radiologist.
Clinical reasoning is broader than answering an image question
A 2024 Nature Medicine study examined 2,400 real patient cases involving four abdominal pathologies and compared evaluated large language models with physicians. It is not a direct VLM image-accuracy benchmark, but it exposes a common validation mistake: licensing-style questions do not represent the full clinical decision process.
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The evaluated systems showed weaknesses in guideline adherence, laboratory interpretation, information gathering, instruction following, and sensitivity to the amount and order of information supplied. A model can identify a visual pattern yet still make an unsafe decision because it asks for the wrong missing test, overlooks a contraindication, or changes its answer when the same facts are reordered. The study’s conclusion was that the systems examined were not ready for autonomous clinical decision-making.
Chest-X-ray reporting: assistance is more plausible than autonomy
A 2024 Nature Medicine evaluation of Flamingo-CXR tested a model fine-tuned to generate chest-X-ray reports. The study used data from United States intensive-care settings and Indian inpatient and outpatient settings and involved 27 board-certified radiologists.
Clinician-edited model reports were rated preferable or equivalent to the original clinician reports by at least half of the panel in 53.6% of MIMIC-CXR cases and 71.2% of IND1 cases. Unedited model reports reached 44.4% and 51.2%, respectively. Preferences differed by dataset and region. These percentages measure panel preference or equivalence under that study’s rubric; they are not diagnostic-accuracy rates and do not show that autonomous reporting is safe.
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What driving benchmarks reveal
DVBench tests difficult video reasoning, not road safety
Zeng and colleagues created DVBench around rare and safety-critical driving scenarios that ordinary driving datasets can underrepresent. The benchmark contains 10,000 human-annotated multiple-choice questions drawn from driving videos. Fourteen vision-language models were evaluated, and none exceeded 40% accuracy under the paper’s GroupEval strategy.
The authors reported improvements after fine-tuning selected models, but those gains do not demonstrate safe real-world driving. DVBench measures video understanding and reasoning on questions; it does not test an end-to-end vehicle operating on public roads, including perception sensors, localization, planning, control, hardware failures, or interactions with unpredictable road users.
A model that chooses the right answer about a recorded event is not automatically capable of selecting a safe braking maneuver milliseconds before a collision. Conversely, a low score on this benchmark does not prove that every component of an automated-driving stack is unsafe. The benchmark is evidence about one important capability, not a certification of an entire vehicle.
The evaluation dimensions that matter
1. Test the real task, not a convenient proxy
Build a test set that mirrors the actual decision and information available at the point of use. If clinicians receive longitudinal records, test longitudinal records. If a driver-assistance system receives synchronized video streams, test synchronized streams rather than still images with a written caption. Include the decisions that trigger treatment, escalation, or a maneuver, not only questions with a neat, known answer.
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2. Use representative data and check external validity
Performance should be measured across the people, institutions, devices, geography, and conditions expected after deployment. A model trained or tuned on one hospital’s scanners may behave differently on another vendor’s equipment. A driving model evaluated in clear daylight may fail in rain, glare, darkness, road construction, or an unfamiliar country. Hold out institutions and time periods when possible so that memorization or local conventions do not inflate results.
3. Report the complete error profile
Average accuracy hides the failures that matter most. Report false-negative and false-positive rates, severity and localization errors, hallucinated findings, omissions, and the frequency of unsafe confident answers. For rare but serious events, provide sensitivity, specificity, positive and negative predictive values, and confidence intervals rather than a single aggregate score.
Examine calibration as well. If a system assigns 80% confidence, approximately eight out of ten such predictions should be correct in the tested population. A useful high-stakes system should also know when to abstain. An abstention that routes a difficult case to a qualified reviewer can be safer than a forced guess, but abstention rates and the cases selected for review must be measured.
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Disaggregate results by clinically or operationally relevant groups, such as age, sex, skin tone where appropriate, disease prevalence, language, scanner type, and care setting. For driving, examine weather, lighting, road design, traffic density, vulnerable road users, and camera placement. Test changes in input order, phrasing, image compression, missing metadata, and partial views. A model that performs well on common examples may still fail on rare findings or new environments.
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5. Evaluate the human-AI workflow
“Human in the loop” is not a safety guarantee. Test the actual interface and sequence of work: when the model displays an answer, how uncertainty is shown, whether the reviewer can inspect the underlying image or evidence, and what happens when the model is unavailable. Measure whether reviewers detect model errors, whether they become overconfident in incorrect suggestions, how long review takes, and who has final authority.
Compare assisted and unassisted performance with the same users and realistic workload. A model that raises individual accuracy but causes automation bias, slower escalation, or missed rare cases may increase overall risk.
6. Plan monitoring and intervention before launch
Performance can change when patient mix, equipment, software versions, road conditions, or user behavior changes. Establish ongoing sampling and review, drift detection, incident reporting, version tracking, and thresholds that trigger investigation or rollback. Keep a safe fallback, such as manual interpretation or a conventional control mode, and define who can pause the system. NIST’s AI Risk Management Framework 1.0 emphasizes validity and reliability, safety, robustness, documentation, and risk management across the lifecycle. It is voluntary general guidance, not a VLM approval standard, and its framework page is being revised; sector-specific rules still apply.
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A practical readiness gate
Before allowing a VLM to influence a consequential decision, an organization should be able to answer “yes” to each applicable question:
- Use case: Is the intended decision and the model’s role written precisely, including prohibited uses?
- Evidence: Has the system been evaluated on representative, held-out data from the intended setting?
- Error costs: Are false negatives, false positives, hallucinations, omissions, and severe edge cases quantified?
- Subgroups: Are relevant demographic, clinical, geographic, equipment, and operating-condition differences reported?
- Robustness: Does performance remain acceptable when inputs are reordered, incomplete, degraded, or shifted from development conditions?
- Uncertainty: Does the system communicate calibrated confidence or abstain usefully instead of forcing an answer?
- Workflow: Has the complete human-AI process been tested, including workload, automation bias, escalation, and accountability?
- Operations: Are monitoring, incident response, model-version control, rollback, and a manual fallback ready?
- Governance: Have applicable medical, transportation, privacy, security, and professional requirements been reviewed?
How to interpret the headline numbers
| Evidence | What was measured | What it supports | What it does not support |
|---|---|---|---|
| Meddeb et al., npj Digital Medicine, 2025 | Five VLMs and neuroradiologists on 100 brain and spine cases; best VLM 35%, specialists 86.2%; harmful outputs up to 45% | A serious reliability gap on the studied neuroradiology cases | A universal ranking of all current medical VLMs or all imaging tasks |
| DVBench, arXiv, 2025 | 14 VLLMs answering 10,000 annotated driving-video questions; no model above 40% under GroupEval | Weaknesses in safety-critical video understanding and reasoning | End-to-end autonomous-driving safety or public-road certification |
| Flamingo-CXR, Nature Medicine, 2024 | Panel preference or equivalence for clinician-edited and unedited reports across MIMIC-CXR and IND1 | A possible assistive reporting workflow worth validating | Diagnostic accuracy or proof that autonomous reporting is safe |
Which uses are more defensible today?
The strongest candidates are bounded tasks where the model’s output is advisory, the input and expected output are tightly specified, a qualified person can verify the result, and a safe alternative remains available. Examples might include drafting a report for clinician editing, organizing images for review, or highlighting cases for a specialist queue—provided each use is validated locally.
Uses that directly determine diagnosis, treatment, emergency triage, driving control, or other irreversible actions require a substantially higher evidentiary bar. General visual-question-answering scores, exam performance, or a vendor’s demonstration cannot substitute for prospective, workflow-level validation and continuous monitoring.
Bottom line
Current evidence does not justify autonomous reliance on general-purpose vision-language models for high-stakes decisions. The 2025 neuroradiology and DVBench results show substantial failures in two consequential domains, while the chest-X-ray study suggests that carefully supervised assistance may help in a defined workflow. Treat “accurate enough” as a system-level determination: validate the exact task, quantify consequential errors, test representative conditions and groups, measure human collaboration, and maintain monitoring and a reliable fallback.
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