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Area of Science:

  • Computational biology
  • Biomedical informatics
  • Artificial intelligence in medicine

Background:

  • Deep learning (DL) models are increasingly utilized in biological research and biomedical applications.
  • DL excels at integrating large datasets and learning complex biological relationships.
  • Current DL applications include predicting genetic variation effects, drug-target interactions, and disease detection from medical images.

Purpose of the Study:

  • To highlight the impact and potential of deep learning in biomedicine.
  • To address the challenges of performance guarantees and trust in deployed DL systems.
  • To propose solutions for enhancing the interpretability and rationale of DL predictions in healthcare.

Main Methods:

  • Review of current deep learning applications in biological research and medicine.
  • Analysis of challenges related to trust, interpretability, and regulation of DL models.
  • Exploration of methods to train DL models for explainable predictions.

Main Results:

  • Deep learning models demonstrate success in predicting various biological and medical outcomes.
  • The flexibility of DL presents challenges in ensuring system reliability and gaining stakeholder trust.
  • Training DL models to provide rationales for their predictions is a key area for future research.

Conclusions:

  • Deep learning holds significant potential to revolutionize biomedical research and applications.
  • Overcoming challenges in interpretability and trust is crucial for widespread clinical adoption.
  • Further research is necessary to develop explainable AI for reliable and trustworthy biomedical DL systems.