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Advancing dental diagnostics with OpenAI's o1-preview: A follow-up evaluation of ChatGPT's performance on diagnostic
Background:
The introduction of o1-preview (OpenAI) has stirred discussions surrounding its potential applications for diagnosing complex patient cases. The authors gauged changes in o1-preview's capacity to diagnose complex cases compared with its predecessors ChatGPT-3.5 (OpenAI) and ChatGPT-4 (legacy) (OpenAI).
Methods:
The authors used diagnostic challenges retrieved from the literature using 2 different approaches to elucidate o1-preview's capacity to produce plausible differential diagnoses (DDs) and final diagnoses (FDs). The first approach instructed the chatbot to independently construct a DD before selecting a final diagnosis. The second approach instructed the chatbot to rely on DDs retrieved from the literature accompanying the diagnostic challenge. A 2-tailed t test was used to compare sample means, and a 2-tailed χ2 test was used to compare sample proportions. A P value < .05 was considered statistically significant.
Results:
The o1-preview model produced a plausible DD and a correct diagnosis for 94% and 80% of cases, respectively, when relying on an independent diagnostic approach, marking a significant increase from ChatGPT-3.5 (DD: difference, 32%; P = .001; FD: difference, 40%; P < .001) and ChatGPT-4 (legacy) (DD: difference, 18%; P = .012; FD: difference, 18%; P = .048). When relying on DDs retrieved from the literature, the model achieved a diagnostic accuracy of 86%, displaying a superior performance than its predecessors, although these results were not significant (ChatGPT-3.5: difference, 16%; P = .055; ChatGPT-4 (legacy): difference, 6%; P = .427).
Conclusions:
Although further validation is required, the transformative findings of this investigation shift the discussion surrounding ChatGPT's integration as a diagnostic tool to be not a question of if, but instead a matter of when.
Practical Implications:
Although o1-preview has yet to achieve a proficient diagnostic accuracy, the model served well in generating DDs for complex cases.
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