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Open Access Original Article Issue
Evaluating ChatGPT's Adherence to Medical Ethics: A Prerequisite for Artificial Intelligence in Medicine
Health Care Science 2026, 5(2): 98-108
Published: 16 April 2026
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Background

As artificial intelligence continues to play an expanding role in healthcare, ensuring its compliance with medical ethics is essential. However, the ethical performance of artificial intelligence in medical contexts remains insufficiently studied. This study aimed to evaluate the ability of ChatGPT to address questions related to medical ethics and to compare its performance with that of human experts.

Methods

A Medical Ethics Evaluation dataset was developed, consisting of 465 single‐choice questions derived from a range of medical ethics standards. These questions were used to assess two artificial intelligence models, GPT‐3.5 and GPT‐4. Model responses were compared with those provided by two medical ethics experts. Each test was conducted independently twice to ensure consistency. Accuracy was calculated for each model and expert, and chi‐square tests were used to compare differences in performance.

Results

GPT‐3.5 achieved an overall accuracy of 38.92%, while GPT‐4 achieved 27.10%. In comparison, two medical ethics experts achieved substantially higher accuracies of 86.23% and 78.32%, respectively. Both experts performed significantly better than GPT‐3.5 and GPT‐4. These findings indicate a substantial gap between artificial intelligence models and human experts in understanding and applying medical ethics principles. The relatively low performance of the models, compared with their reported strengths in diagnostic tasks, may reflect the complexity and nuance of ethical reasoning in medicine. Nevertheless, the large language models showed some ability to align with core medical ethics principles, particularly in ethical dilemma scenarios, and were also able to generate responses that addressed psychological needs.

Conclusions

Artificial intelligence models currently show limited accuracy in medical ethics decision‐making compared with human experts. Although these models demonstrate some alignment with fundamental ethical principles, the performance is not yet sufficient for reliable use in ethically sensitive medical contexts. Further optimization is needed to improve their ability to meet the ethical demands of medical practice.

Open Access Issue
Towards Artificial Intelligence for Science: A Case Study of Using ChatGPT for Disease Causality Discovery from Biomedical Literature
Big Data Mining and Analytics 2026, 9(2): 554-562
Published: 09 February 2026
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Generative artificial intelligence (GAI) has garnered considerable attention from the scientific community and driven a paradigm shift in scientific research, namely AI for science (AI4S). To evaluate the potential of GAI in biomedical research, this study explores the capability of using large language models (LLMs) to discover biomedical knowledge by conducting a case study to extract disease causalities from large-scale biomedical literature using ChatGPT. In particular, two groups of the most frequent causal knowledge in the biomedical domain are discussed in this study, especially the causalities among different diseases and chemically induced causations. Quantitatively, ChatGPT’s responses are compared with biocurators’ annotations in identifying causalities from a random sampling of PubMed citations. Guided by six predefined prompts of three mainstream paradigms, namely zero-shot, few-shot, and long chain-of-thought (CoT), ChatGPT’s results are very close to human annotations in terms of a high degree of precision (94.73% for the disease-disease group and 92.47% for the chemical-disease group individually). These findings suggest that ChatGPT has the capacity to discover biomedical knowledge efficiently, as well as the capability to facilitate biomedical research. In the near future, more state-of-the-art LLMs, more real-world data, such as electronic medical records, and more biomedical tasks will be pursued for a series of holistic studies. Overall, this work supports the vision and development of AI4S, especially the knowledge discovery of biomedical research.

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