Diagnostic Performance Comparison between Generative AI and Physicians: A Systematic Review and Meta-Analysis
Authors: Takita Hirotaka , Kabata Daijiro , Walston Shannon L , Tatekawa Hiroyuki , Saito Kenichi , Tsujimoto Yasushi , Miki Yukio , Ueda Daiju 
Published: January 2024

Background

The rapid advancement of generative artificial intelligence (AI) has led to the wide dissemination of models with exceptional understanding and generation of human language. Their integration into healthcare has shown potential for improving medical diagnostics, yet a comprehensive diagnostic performance evaluation of generative AI models and the comparison of their diagnostic performance with that of physicians has not been extensively explored.

Methods

In this systematic review and meta-analysis, a comprehensive search of Medline, Scopus, Web of Science, Cochrane Central, and MedRxiv was conducted for studies published from June 2018 through December 2023, focusing on those that validate generative AI models for diagnostic tasks. The risk of bias was assessed using the Prediction Model Study Risk of Bias Assessment Tool. Meta-regression was performed to summarize the performance of the models and to compare the accuracy of the models with that of physicians.

Results

The search resulted in 54 studies being included in the meta-analysis. Nine generative AI models were evaluated across 17 medical specialties. The quality assessment indicated a high risk of bias in the majority of studies, primarily due to small sample sizes. The overall accuracy for generative AI models across 54 studies was 56.9% (95% confidence interval [CI]: 51.0–62.7%). The meta-analysis demonstrated that, on average, physicians exceeded the accuracy of the models (difference in accuracy: 14.4% [95% CI: 4.9–23.8%], p-value =0.004). However, both Prometheus (Bing) and GPT-4 showed slightly better performance compared to non-experts (-2.3% [95% CI: -27.0–22.4%], p-value = 0.848 and -0.32% [95% CI: -14.4–13.7%], p-value = 0.962), but slightly underperformed when compared to experts (10.9% [95% CI: -13.1–35.0%], p-value = 0.356 and 12.9% [95% CI: 0.15–25.7%], p-value = 0.048). The sub-analysis revealed significantly improved accuracy in the fields of Gynecology, Pediatrics, Orthopedic surgery, Plastic surgery, and Otolaryngology, while showing reduced accuracy for Neurology, Psychiatry, Rheumatology, and Endocrinology compared to that of General Medicine. No significant heterogeneity was observed based on the risk of bias.

Conclusions

Generative AI exhibits promising diagnostic capabilities, with accuracy varying significantly by model and medical specialty. Although they have not reached the reliability of expert physicians, the findings suggest that generative AI models have the potential to enhance healthcare delivery and medical education, provided they are integrated with caution and their limitations are well-understood.

Key Points

Question:What is the diagnostic accuracy of generative AI models and how does this accuracy compare to that of physicians?

Findings:This meta-analysis found that generative AI models have a pooled accuracy of 56.9% (95% confidence interval: 51.0–62.7%). The accuracy of expert physicians exceeds that of AI in all specialties, however, some generative AI models are comparable to non-expert physicians.

Meaning:The diagnostic performance of generative AI models suggests that they do not match the level of experienced physicians but that they may have potential applications in healthcare delivery and medical education.