Multimodal Artificial Intelligence in Medicine - Conor S. Judge; Finn Krewer; Martin J. O'Donnell; Lisa Kiely; Donal Sexton; Graham W. Taylor; Joshua August Skorburg; Bryan Tripp (Kidney360)
Traditional medical artificial intelligence models that are approved for clinical use restrict themselves to single-modal data (e.g., images only), limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal transformer models in health care can effectively process and interpret diverse data forms, such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks, like United States Medical Licensing Examination question banks, and continue to improve with scale. However, the adoption of these advanced artificial intelligence models is not without challenges. While multimodal deep learning models like transformers offer promising advancements in health care, their integration requires careful consideration of the accompanying ethical and environmental challenges.