Enhancing accuracy and interpretability in EEG-based medical decision making using an explainable ensemble learning framework application for stroke prediction

Bouazizi, Samar; Ltifi, Hela

doi:10.1016/j.dss.2023.114126

Cited by 4 publications

(1 citation statement)

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“…One concern is the "black-box" nature of many algorithms, where the decisionmaking process remains opaque. In healthcare applications, it is necessary to ensure trust and improve comprehension among healthcare practitioners in clinical decision-making [81]. Enhancing interpretability in clinical decision-making leads to a better understanding of future predictions, empowering healthcare experts for personalized decisions, and improving healthcare service quality [82].…”

Section: ) Rq1mentioning

confidence: 99%

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

Shafik,

Hidayatullah,

Kalinaki

et al. 2024

Preprint

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The increased utilization of disruptive health and biomedical informatics technologies, such as artificial intelligence (AI), has accelerated medical operations from patient-centered medical experience data management to simplified medical procedures in this generative era. As these technologies integrate into traditional approaches, they raise critical medical concerns, entailing transparency and interpretability of these AI models. This study conducts a systematic literature review (SLR) and presents an exhaustive review of the studies using data collection procedures and publicly available academic databases. 1837 articles published between 2014 and 2024 were obtained from eight popular academic databases: PubMed, ACM Library, Springer, Scopus, IEEE Xplore, ScienceDirect, Google Scholar, and Web of Science. A comprehensive screening process was used, and 148 articles were considered based on the relevance of the AI method to healthcare and biomedical. The studied studies demonstrate that the majority of medical people still find it complex to effectively explain the reasoning behind the decisions AI models make during biomedical experiments, leading to limited trust, biased decision-making, and unknown patient data safety. Model-agnostic strategies and explainable AI (XAI) frameworks are inspected, together with crucial datasets for training and assessment. The main challenges are AI model intricacy and regulatory compliance, while future trends highlight fairness and predisposition mitigation. Limited studies are focusing on improving AI openness, trust, and interpretability. Towards the end, it reveals that there is still a big research gap in descriptive explainable AI models in healthcare when integrating AI into clinical practice while maintaining ethical standards and patient-centric care.

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Section: ) Rq1mentioning

confidence: 99%