Evaluating the Precision of ChatGPT Artificial Intelligence in Emergency Differential Diagnosis
Bibliographic record
Abstract
Introduction: artificial intelligence (AI) is the study and development of intelligent machines that can carry out tasks that would typically require human intelligence. AI seeks to give machines the ability to think, problem-solve, sense their surroundings, and comprehend human speech. By enhancing and optimising processes, this technology is predicted to completely transform a number of industries. Artificial intelligence is tipped to be the next technological breakthrough that will shape our future. Objective: This study focused on evaluating the precision of ChatGPT artificial intelligence in emergency differential diagnosis. Methods: This was a comparison study, conducted from August to September 2023, evaluating the ability of both the Monica ChatGPT and the emergency medicine textbooks to provide differential diagnoses for frequently occurring complaints. Twelve symptoms common to adult patients were included in the list of chief complaints. To gauge the accuracy of the ChatGPT’s answers, the researcher employed ChatGPT®-4 queries. Results: The total number of differential diagnoses captured by the two resources was 431. The ChatGPT captured a total of 272 differential diagnoses; however, 59 of these were not included in the list of the chief complaints. Conclusion: The study concludes that AI can be helpful in some situations, such as primary care diagnosis and patient triage, although in most cases it is not a better diagnostic tool. Therefore, AI and human diagnosis can be used concurrently in the health sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".