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Area of Science:

  • Clinical Medicine
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Artificial Intelligence (AI) technologies are increasingly integrated into clinical medicine.
  • Large language models (LLMs) and multimodal systems are being applied across various medical domains, including communication, imaging, and predictive analytics.

Purpose of the Study:

  • To review the advancements and applications of AI, particularly LLMs, in clinical medicine.
  • To highlight progress in areas like clinical summaries, patient messaging, and decision support.
  • To identify persistent challenges and future directions in AI for healthcare.

Main Methods:

  • Review of recent advances in generative and retrieval-augmented AI methods.
  • Analysis of new benchmarks in medical imaging, vision, and spontaneous speech processing.
  • Examination of predictive modeling techniques focusing on causality and clinical events.
  • Assessment of methodological contributions in uncertainty management and interpretable AI.

Main Results:

  • Improved accuracy and contextual grounding in clinical summaries, patient messaging, and decision support systems.
  • Significant progress in AI capabilities for medical imaging and speech analysis, alongside recognition of remaining challenges.
  • Advancements in predictive modeling for understanding disease trajectories and clinical events.
  • Development of frameworks for AI evaluation and governance to bridge the gap between research and clinical practice.

Conclusions:

  • AI, especially LLMs, shows significant potential to enhance clinical medicine through improved communication and analytics.
  • Addressing challenges in AI evaluation, governance, and real-world deployment is crucial for successful integration into healthcare.
  • Continued research in predictive modeling and uncertainty management will further refine AI's role in clinical decision-making.