Fairness of artificial intelligence in healthcare: review and recommendations

Ueda, Daiju; Kakinuma, Taro; Fujita, Shohei; Kamagata, Koji; Fushimi, Yasutaka; Ito, Rintaro; Matsui, Yusuke; Nozaki, Taiki; Nakaura, Takeshi; Fujima, Noriyuki; Tatsugami, Fuminari; Yanagawa, Masahiro; Hirata, Kenji; Yamada, Akira; Tsuboyama, Takahiro; Kawamura, Mitsuo; Fujioka, Tomoyuki; Naganawa, Shinji

doi:10.1007/s11604-023-01474-3

Cited by 117 publications

(29 citation statements)

References 125 publications

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“…Transparency ensures an understanding of the model’s knowledge, context, and limitations, aids in identifying potential biases, and facilitates independent replication and validation, which are fundamental to scientific integrity. As generative AI continues to evolve, fostering a culture of rigorous transparency is essential to ensure their safe, effective, and equitable application in clinical settings, 75 ultimately enhancing the quality of healthcare delivery and medical education.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Performance Comparison between Generative AI and Physicians: A Systematic Review and Meta-Analysis

Takita,

Kabata,

Walston

et al. 2024

Preprint

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Background: The rapid advancement of generative artificial intelligence (AI) has revolutionized 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. Meta-analysis was performed to summarize the performance of the models and to compare the accuracy of the models with that of physicians. The quality of studies was assessed using the Prediction Model Study Risk of Bias Assessment Tool. Results: The search resulted in 54 studies being included in the meta-analysis, with 13 of these also used in the comparative analysis. Eight models were evaluated across 17 medical specialties. The overall accuracy for generative AI models across 54 studies was 57% (95% confidence interval [CI]: 51–63%). The I-squared statistic of 96% signifies a high degree of heterogeneity among the study results. Meta-regression analysis of generative AI models revealed significantly improved accuracy for GPT-4, and reduced accuracy for some specialties such as Neurology, Endocrinology, Rheumatology, and Radiology. The comparison meta-analysis demonstrated that, on average, physicians exceeded the accuracy of the models (difference in accuracy: 14% [95% CI: 8–19%], p-value <0.001). However, in the performance comparison between GPT-4 and physicians, GPT-4 performed slightly higher than non-experts (-4% [95% CI: -10–2%], p-value = 0.173), and slightly underperformed compared to experts (6% [95% CI: -1–13%], p-value = 0.091). The quality assessment indicated a high risk of bias in the majority of studies, primarily due to small sample sizes. 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. This study also highlights the need for more rigorous research standards and a larger number of cases in the future.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Performance Comparison between Generative AI and Physicians: A Systematic Review and Meta-Analysis

Takita,

Kabata,

Walston

et al. 2024

Preprint

Self Cite

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“…This study is the first attempt to evaluate ChatGPT's ability to interpret actual clinical radiology reports, rather than from settings like image diagnosis quizzes. The majority of previous research (6)(7)(8)(9)(10)(11)(12)(17)(18)(19)(20)(21) suggested the utility of ChatGPT in diagnostics, but these relied heavily on hypothetical environments such as quizzes from academic journals or examination questions (26). This approach can lead to a cognitive bias since the individuals formulating the imaging findings or exam questions also possess the answers.…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of ChatGPT’s Diagnostic Performance with Radiologists Using Real-World Radiology Reports of Brain Tumors

Mitsuyama,

Tatekawa,

Takita

et al. 2023

Preprint

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Background Large Language Models like Chat Generative Pre-trained Transformer (ChatGPT) have demonstrated potential for differential diagnosis in radiology. Previous studies investigating this potential primarily utilized quizzes from academic journals, which may not accurately represent real-world clinical scenarios. Purpose This study aimed to assess the diagnostic capabilities of ChatGPT using actual clinical radiology reports of brain tumors and compare its performance with that of neuroradiologists and general radiologists. Methods We consecutively collected brain MRI reports from preoperative brain tumor patients at Osaka Metropolitan University Hospital, taken from January to December 2021. ChatGPT and five radiologists were presented with the same findings from the reports and asked to suggest differential and final diagnoses. The pathological diagnosis of the excised tumor served as the ground truth. Chi-square tests and Fisher′s exact test were used for statistical analysis. Results In a study analyzing 99 radiological reports, ChatGPT achieved a final diagnostic accuracy of 75% (95% CI: 66, 83%), while radiologists′ accuracy ranged from 64% to 82%. ChatGPT′s final diagnostic accuracy using reports from neuroradiologists was higher at 82% (95% CI: 71, 89%), compared to 52% (95% CI: 33, 71%) using those from general radiologists with a p-value of 0.012. In the realm of differential diagnoses, ChatGPT′s accuracy was 95% (95% CI: 91, 99%), while radiologists′ fell between 74% and 88%. Notably, for these differential diagnoses, ChatGPT's accuracy remained consistent whether reports were from neuroradiologists (96%, 95% CI: 89, 99%) or general radiologists (91%, 95% CI: 73, 98%) with a p-value of 0.33. Conclusion ChatGPT exhibited good diagnostic capability, comparable to neuroradiologists in differentiating brain tumors from MRI reports. ChatGPT can be a second opinion for neuroradiologists on final diagnoses and a guidance tool for general radiologists and residents, especially for understanding diagnostic cues and handling challenging cases.

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“…Related to these concerns is the demand for explainability of AI models, which in turn also needs to be considered when AI technology is to be used for pathway modelling, or for the integration of a chatbot or other AI supported functions into digital patient pathways. Stakeholders interested in implementing digital patient pathways ought to keep that demand in mind, which is also already widely being discussed by researchers [see, e.g., (133)(134)(135)].…”

Section: Walking Along the Pathway: Ms Treatment Could Change For The...mentioning

confidence: 99%

Building digital patient pathways for the management and treatment of multiple sclerosis

Wenk,

Voigt,

Inojosa

et al. 2024

Front. Immunol.

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Recent advances in the field of artificial intelligence (AI) could yield new insights into the potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI opens new possibilities regarding the interpretation and use of big data from not only a cross-sectional, but also a longitudinal perspective. For each patient with MS, there is a vast amount of multimodal data being accumulated over time. But for the application of AI and related technologies, these data need to be available in a machine-readable format and need to be collected in a standardized and structured manner. Through the use of mobile electronic devices and the internet it has also become possible to provide healthcare services from remote and collect information on a patient’s state of health outside of regular check-ups on site. Against this background, we argue that the concept of pathways in healthcare now could be applied to structure the collection of information across multiple devices and stakeholders in the virtual sphere, enabling us to exploit the full potential of AI technology by e.g., building digital twins. By going digital and using pathways, we can virtually link patients and their caregivers. Stakeholders then could rely on digital pathways for evidence-based guidance in the sequence of procedures and selection of therapy options based on advanced analytics supported by AI as well as for communication and education purposes. As far as we aware of, however, pathway modelling with respect to MS management and treatment has not been thoroughly investigated yet and still needs to be discussed. In this paper, we thus present our ideas for a modular-integrative framework for the development of digital patient pathways for MS treatment.

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Fairness of artificial intelligence in healthcare: review and recommendations

Cited by 117 publications

References 125 publications

Diagnostic Performance Comparison between Generative AI and Physicians: A Systematic Review and Meta-Analysis

Diagnostic Performance Comparison between Generative AI and Physicians: A Systematic Review and Meta-Analysis

Comparative Analysis of ChatGPT’s Diagnostic Performance with Radiologists Using Real-World Radiology Reports of Brain Tumors

Building digital patient pathways for the management and treatment of multiple sclerosis

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