Session Introduction: AI and Machine Learning in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface.

Journal: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
PMID: 39670359

Abstract

Artificial Intelligence (AI) technologies are increasingly capable of processing complex and multilayered datasets. Innovations in generative AI and deep learning have notably enhanced the extraction of insights from both unstructured texts, images, and structured data alike. These breakthroughs in AI technology have spurred a wave of research in the medical field, leading to the creation of a variety of tools aimed at improving clinical decision-making, patient monitoring, image analysis, and emergency response systems. However, thorough research is essential to fully understand the broader impact and potential consequences of deploying AI within the healthcare sector.

Authors

  • Fateme Nateghi Haredasht
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
  • Dokyoon Kim
    Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA.
  • Joseph D Romano
    Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Geoff Tison
  • Roxana Daneshjou
    1Department of Biomedical Data Science, Stanford School of Medicine, Stanford, California, USA; email: roxanad@stanford.edu.
  • Jonathan H Chen
    Stanford Center for Biomedical Informatics Research, Stanford, CA.

Keywords

External Resources

Popular Topics
Recent Journals