Development of Interpretable Deep Learning-Based Clinical Decision Support Systems

Background and Aims: Artificial Intelligence (AI)-driven Clinical Decision Support Systems (CDSS) are transformative tools in digital health. This study focuses on designing and evaluating a novel CDSS framework that integrates clinical, imaging, and patient metadata to assist physician decision-making. The core innovation lies in combining deep learning with interpretability methods (XAI) to enhance system trustworthiness in clinical settings. Methods: This study developed a deep learning-based clinical decision support system (CDSS) using MIMIC-III (clinical data) and CheXpert (chest X-rays). Data were standardized via FHIR, with Transformers analyzing medical reports and ResNet-۵۰ detecting anomalies. Federated Learning preserved privacy, while SHAP and Grad-CAM ensured interpretability. The system achieved ۹۳.۴% accuracy in diagnosis extraction and ۹۴.۱% sensitivity in imaging. Innovations included multimodal fusion (GNNs + Transformers) and privacy-aware training, balancing speed, accuracy, and transparency for clinical adoption. Results: Diagnosis: The NLP model extracted complex conditions (e.g., sepsis) from clinical reports with ۹۲.۴% accuracy, leveraging deep learning's ability to parse unstructured text. ResNet-۵۰, a convolutional neural network (CNN), identified radiological abnormalities with ۹۴.۱% sensitivity, demonstrating its efficacy in medical image analysis. Interpretability: SHAP analysis revealed that key features like CRP levels and tumor patterns in imaging most influenced model predictions, addressing the 'black-box' critique of AI in healthcare. Privacy: Federated Learning increased model training time by only ۱۵% while ensuring data security, a critical advancement for multi-institutional collaborations. Challenges and Innovations: Technical Integration: Combining diverse data types (e.g., imaging, genomics) requires robust architectures like CNNs for image processing and Transformers for NLP. Clinical Adoption: Resistance to AI tools persists due to transparency concerns. XAI methods like SHAP and Grad-CAM provide visual explanations (e.g., highlighting tumor regions in MRI), fostering clinician trust. Ethical Considerations: Data privacy and algorithmic bias remain critical, necessitating frameworks like Federated Learning and standardized XAI protocols. Conclusion: This CDSS framework bridges deep learning's predictive power with interpretability, advancing personalized medicine. However, challenges such as EHR integration and clinician acceptance require interdisciplinary collaboration. Future work should prioritize real-world validation and addressing disparities in underrepresented populations.

کلیدواژه ها:

Artificial Intelligence ، Digital Health ، Clinical Decision