4.6 Review

A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System

Journal

SENSORS
Volume 22, Issue 20, Pages -

Publisher

MDPI
DOI: 10.3390/s22208068

Keywords

eXplainable Artificial Intelligence (XAI); XAI recommendation system; XAI scoring system; medical XAI; survey; approach

Funding

  1. Ministry of Science and Technology (MOST), Taiwan [MOST 111-2321-B-075A-001]

Ask authors/readers for more resources

The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of utmost importance. This survey provides a detailed investigation of medical XAI, including model enhancements, evaluation methods, case studies, open datasets, and future improvements. It also discusses the differences in AI and XAI methods, as well as the importance of XAI characteristics and explainability in healthcare.
The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of utmost importance. Meanwhile, incorporating explanations in the medical domain with respect to legal and ethical AI is necessary to understand detailed decisions, results, and current status of the patient's conditions. Successively, we will be presenting a detailed survey for the medical XAI with the model enhancements, evaluation methods, significant overview of case studies with open box architecture, medical open datasets, and future improvements. Potential differences in AI and XAI methods are provided with the recent XAI methods stated as (i) local and global methods for preprocessing, (ii) knowledge base and distillation algorithms, and (iii) interpretable machine learning. XAI characteristics details with future healthcare explainability is included prominently, whereas the pre-requisite provides insights for the brainstorming sessions before beginning a medical XAI project. Practical case study determines the recent XAI progress leading to the advance developments within the medical field. Ultimately, this survey proposes critical ideas surrounding a user-in-the-loop approach, with an emphasis on human-machine collaboration, to better produce explainable solutions. The surrounding details of the XAI feedback system for human rating-based evaluation provides intelligible insights into a constructive method to produce human enforced explanation feedback. For a long time, XAI limitations of the ratings, scores and grading are present. Therefore, a novel XAI recommendation system and XAI scoring system are designed and approached from this work. Additionally, this paper encourages the importance of implementing explainable solutions into the high impact medical field.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available