DESIGNING TRUSTWORTHY CLINICAL DECISION SUPPORT SYSTEMS USING DEEP LEARNING AND HUMAN-IN-THE-LOOP VALIDATION
Abstract
Deep learning has proven to be potentially useful in medical diagnosis, as it can be used to automatically analyze complex healthcare data, including medical images and electronic health records. Although there are positive outcomes of deep learning models, their implementation in everyday clinical practice is still relatively scarce, even despite encouraging results in controlled research environments. Some of the main issues are that deep neural networks are often black box models, have low interpretability, frequently face ethical responsibility issues, and lack clinician involvement in automated decisions. The majority of existing literature focuses more on predictive accuracy, and not on system-level considerations required to ensure safe and reliable clinical deployment. The given paper introduces a holistic clinical decision support framework, which combines the deep learning with explainable artificial intelligence and human-in-the-loop validation in order to improve the reliability and clinical acceptance. The suggested framework will be human friendly where deep learning models act as aiding systems and not decision makers, and the clinical accountability will remain with the health professionals. They perform lightweight experimental evaluation by using publicly available medical datasets and pretrained deep learning architectures and to prove that it is feasible without a large amount of computational complexity. Performance analysis and qualitative interpretability assessment suggests that satisfactory diagnostic support may be obtained in the case of transfer learning as implemented in the context of structured clinician supervision. The suggested framework covers profound gaps regarding transparency, trust, and ethical governance of healthcare artificial intelligence and offers a practical base of the creation of trustful and clinically deployable decision support systems based on deep learning.
