Improving tractability of Clinical Decision Support system

In AI-based CDSS particularly, XAI did not appear until nearly a decade into the 2000s.

“In AI-based CDSS particularly, XAI did not appear until nearly a decade into the 2000s [124].”

Rule-based systems were developed in two studies to provide local explanations, as they were considered closer to human reasoning and thus more preferable by clinicians.

“Rule-based systems were developed in two studies to provide local explanations, as they were considered closer to human reasoning, and thus more preferable by clinicians [124,125].”

XAI could greatly enhance the effectiveness of decision-support and clinician confidence, especially when high-stakes decisions are being made, which is the key factor for the success of the model in the practical use stage.

“XAI therefore could greatly enhance the effectiveness of decision-support and clinician confidence [128] especially when high-stakes decisions are being made [127], which is the key factor for the success of the model in the practical use stage [122,124].”

Tan et al. argue that a CDSS should have high tractability, which requires human like reasoning, step by step inference, explanation capacity, and user-familiar terms, to gain user acceptance.

“Tan et al. [124] argue that a CDSS should have high tractability, which requires human like reasoning, step by step inference, explanation capacity, and user-familiar terms, to gain user acceptance.”

Most reviewed studies implemented a model-specific ante-hoc technique that provided local explanations.

“Most studies implemented a model-specific ante-hoc technique that provided local explanations [122,124-126,128].”

Tan et al. [124] used CLFNN, which autonomously generates fuzzy rules to provide human-like reasoning; the method was specific, ante-hoc, and local.

“Tan et al. [124] CLFNN, which autonomously generates fuzzy rules to provide human-like reasoning specific ante-hoc local”