Abstract
AbstractArtificial intelligence (AI) has been extensively researched in medicine, but its practical application remains limited. Meanwhile, there are various disparities in existing AI-enabled clinical studies, which pose a challenge to global health equity. In this study, we conducted an in-depth analysis of the geo-economic distribution of 159 AI-enabled clinical studies, as well as the gender disparities among these studies. We aim to reveal these disparities from a global literature perspective, thus highlighting the need for equitable access to medical AI technologies.
Funder
Ministry of Health, Singapore
Publisher
Springer Science and Business Media LLC
Reference16 articles.
1. Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).
2. Hinton, G. Deep learning—a technology with the potential to transform health care. JAMA 320, 1101–1102 (2018).
3. Woo, M. An AI boost for clinical trials. Nature 573, S100–S102 (2019).
4. Yang, R. et al. Large language models in health care: development, applications, and challenges. Health Care Sci. 2, 255–263 (2023).
5. Ke, Y. H. et al. Enhancing diagnostic accuracy through multi-agent conversations: using large language models to mitigate cognitive bias. Preprint at arXiv:1504.14589 (2024).