Precision of artificial intelligence in paediatric cardiology multimodal image interpretation
Bibliographic record
Abstract
Abstract Multimodal imaging is crucial for diagnosis and treatment in paediatric cardiology. However, the proficiency of artificial intelligence chatbots, like ChatGPT-4, in interpreting these images has not been assessed. This cross-sectional study evaluates the precision of ChatGPT-4 in interpreting multimodal images for paediatric cardiology knowledge assessment, including echocardiograms, angiograms, X-rays, and electrocardiograms. One hundred multiple-choice questions with accompanying images from the textbook Pediatric Cardiology Board Review were randomly selected. The chatbot was prompted to answer these questions with and without the accompanying images. Statistical analysis was done using X2, Fisher’s exact, and McNemar tests. Results showed that ChatGPT-4 answered 41% of questions with images correctly, performing best on those with electrocardiograms (54%) and worst on those with angiograms (29%). Without the images, ChatGPT-4’s performance was similar at 37% (difference = 4%, 95% confidence interval (CI) –9.4% to 17.2%, p = 0.56). The chatbot performed significantly better when provided the image of an electrocardiogram than without (difference = 18, 95% CI 4.0% to 31.9%, p < 0.04). In cases of incorrect answers, ChatGPT-4 was more inconsistent with an image than without (difference = 21%, 95% CI 3.5% to 36.9%, p < 0.02). In conclusion, ChatGPT-4 performed poorly in answering image-based multiple-choice questions in paediatric cardiology. Its accuracy in answering questions with images was similar to without, indicating limited multimodal image interpretation capabilities. Substantial training is required before clinical integration can be considered. Further research is needed to assess the clinical reasoning skills and progression of ChatGPT in paediatric cardiology for clinical and academic utility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".