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Not overall, based on the available evidence. A 2025 review found no statistically significant difference between generative AI and physicians across the studies it pooled, but the AI systems performed significantly worse than expert physicians. That does not prove the two are equivalent, or tell you whether a particular chatbot can assess your symptoms safely.
What studies report about AI versus doctors
The broadest direct comparison in the evidence reviewed here is a 2025 systematic review and meta-analysis by Takita and colleagues, published in npj Digital Medicine on March 22, 2025. It included 83 studies, with a literature search covering studies published from June 2018 through June 2024. Across those studies, the review found no statistically significant overall performance difference between generative AI and physicians (p=0.10), or between generative AI and non-expert physicians (p=0.93). Generative AI performed significantly worse than expert physicians (p=0.007).
“No statistically significant difference” is not the same as proof of equal performance. The studies varied in their tasks, models, specialties, and conditions, and most were judged to have a high risk of bias. The pooled results therefore describe a diverse body of research, not a guarantee about any one AI tool or diagnosis.
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The review estimated pooled diagnostic accuracy for generative AI at 52.1%, with a 95% confidence interval of 47.0% to 57.1%. This is an aggregate across different studies and tasks. It is not a current chatbot’s individual chance of being right, nor a personalized estimate for your symptoms.
#1 Best Overall
Why a case-study answer is not the same as diagnosing a patient
A model may answer a written case using information already selected and supplied to it. A clinician must also decide what to ask, which examination or tests are needed, how to interpret the results, and how the findings fit together. Those are different demands.
In a 2024 Nature Medicine study, Hager and colleagues evaluated large language models using 2,400 real patient cases derived from MIMIC-IV. The cases covered four abdominal conditions: appendicitis, cholecystitis, diverticulitis, and pancreatitis. The authors reported that performance declined when models had to gather diagnostic information themselves. They also identified problems involving examination requests, guideline adherence, laboratory interpretation, instruction-following, and sensitivity to the order and amount of information presented. They concluded that the LLMs evaluated were not ready for autonomous clinical decision-making and called for extensive clinician supervision.
This study is relevant to the gap between answering a supplied vignette and managing a clinical workflow, but its cases covered four conditions rather than medicine as a whole. It does not establish how every current model performs in every setting.
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“AI” can mean different kinds of medical tools
A general-purpose text chatbot and a task-specific system that analyzes medical images are not interchangeable. They take different inputs, are designed for different tasks, and must be judged against evidence for those tasks. Results from a narrow image-classification study cannot establish that a conversational chatbot can diagnose symptoms or conduct a full medical assessment.
A 2024 review by Salinas and colleagues examined AI algorithms and clinicians classifying skin cancer from dermoscopic images. Across the included studies and clinician subgroups, the review reported sensitivity of 87.0% and specificity of 77.1% for AI algorithms, compared with sensitivity of 79.78% and specificity of 73.6% for all clinicians. In the expert subgroup, the reported AI and expert-dermatologist results were clinically comparable. These figures apply to the review’s dermoscopic skin-cancer classification studies—not to general-purpose chatbots or diagnosis across medicine. The review was published on May 14, 2024, and received an author correction on May 24, 2024.
How to compare the findings fairly
| Evidence | What was evaluated | What the authors reported | What the result applies to |
|---|---|---|---|
| Takita et al., 2025, npj Digital Medicine | Meta-analysis of 83 studies; literature search through June 2024 | Generative AI pooled accuracy: 52.1% (95% CI 47.0–57.1%). No significant overall difference versus physicians (p=0.10) or non-expert physicians (p=0.93); significantly worse than expert physicians (p=0.007). | The varied generative-AI diagnostic tasks and study conditions included in the review, not any one product or patient. |
| Hager et al., 2024, Nature Medicine | 2,400 real patient cases involving appendicitis, cholecystitis, diverticulitis, and pancreatitis; models were also assessed on gathering diagnostic information | Performance declined when models had to gather information themselves; the authors concluded that the evaluated LLMs were not ready for autonomous clinical decisions. | The study’s four abdominal pathologies and clinical-decision tasks, not all medical conditions or all AI systems. |
| Salinas et al., 2024, npj Digital Medicine | Review of dermoscopic skin-cancer classification studies | AI algorithms: sensitivity 87.0%, specificity 77.1%. All clinicians: sensitivity 79.78%, specificity 73.6%; AI and expert dermatologists had clinically comparable reported results in the expert subgroup. | Classification of skin cancer from dermoscopic images, not symptom assessment by a general-purpose chatbot. |
These results answer different questions. A useful comparison needs to specify what the system was asked to do, what information it received, what population and condition were tested, and whether the comparator was a generalist or an experienced specialist. It also matters whether evaluation used curated or internal data, external data, or a real-world clinical workflow.
Rank #4
What patients should do with an AI-generated answer
- Treat it as information to discuss, not a confirmed diagnosis. A chatbot’s written explanation is not the same as a clinical assessment that includes history-taking, examination, and decisions about tests.
- Do not use confident wording as evidence of accuracy. The reviewed results varied by model and task, and the meta-analysis found substantial study-quality concerns.
- Contact a qualified clinician about symptoms that concern you, are worsening, or need assessment. The studies described here do not establish symptom-specific emergency thresholds or a complete triage protocol.
- Protect sensitive information. The evidence summarized here does not establish the privacy terms or data handling of any particular AI service. Do not assume a service is appropriate for sensitive medical records without checking its applicable terms and policies.
How to assess an AI accuracy claim
Look for the details that determine what a reported result can actually tell you:
Quick Recap
Best Value
- The task: Was the system classifying an image, answering a supplied case, or gathering information in a clinical workflow?
- The input: Did it receive a complete case or image, or was it expected to elicit relevant information and request tests?
- The scope: Which condition, population, specialty, and setting were included?
- The comparison: Was AI compared with non-experts, general physicians, or specialists with relevant expertise?
- The evaluation: Were cases curated or internal to the study, externally evaluated, or assessed in real-world practice?
- The reporting: Are limitations, possible bias, and applicability to other populations described? The STARD-AI reporting guideline, published in Nature Medicine in 2025, calls for transparent reporting of datasets, the AI test and evaluation, and bias and fairness considerations. It is a reporting standard, not a patient-care recommendation or evidence that a particular product is authorized.
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