Literature Review Outline

Implementing Deep Learning Tools and/or Techniques in Medical Diagnosis

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1. Introduction
   • Overview of the transformative impact of deep learning (DL) on medical diagnosis.
   • Rationale for DL adoption in healthcare
   • Summary of review scope and methodology.
2. Key DL Techniques in Medical Diagnosis
   • Convolutional Neural Networks (CNNs)
     • Applications.
     • Limitations.
     • Emerging explainability methods.
   • Recurrent Neural Networks (RNNs)
     • Applications in ECGs and EHRs.
     • Strengths and Limitations.
   • Natural Language Processing (NLP)
     • Applications in the analysis of clinical texts and EHRs.
     • Use of transformer models for text classification and summarisation.
     • Limitations.
3. DL Applications Across Medical Fields
   • Radiology and pathology (e.g., cancer detection).
   • Predictive analytics and early disease detection in ICUs and EHR systems.
   • Decision support systems in real-time clinical workflows.
4. Challenges in DL Implementation
   • Data quality and bias.
   • Model interpretability/black-box nature.
   • Computational and infrastructure limitations.
   • Ethical and regulatory concerns.
5. Conclusion
   • Summary of DL’s contributions and limitations in healthcare.
   • Call for responsible integration into clinical practice.