Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines

Advancements in deep learning techniques carry the potential to make significant contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis, prognosis, and treatment decisions. The current state-of-the-art deep learning models for radiology applications consider only pixel-value information without data informing clinical context. Yet in practice, pertinent and accurate non-imaging data based on the clinical history and laboratory data enable physicians to interpret imaging findings in the appropriate clinical context, leading to a higher diagnostic accuracy, informative clinical decision making, and improved patient outcomes. To achieve a similar goal using deep learning,…

• NI
  National Institutes of Health

  Award: R01LM012966
• UN
  U.S. National Library of Medicine

  Award: R01LM012966