Zahra Sadeghi, Roohallah Alizadehsani, Mehmet Akif CIFCI, Samina Kausar, Rizwan Rehman, Priyakshi Mahanta, Pranjal Kumar Bora, Ammar Almasri, Rami S. Alkhawaldeh, Sadiq Hussain, Bilal Alatas, Afshin Shoeibi, Hossein Moosaei, Milan Hladík, Saeid Nahavandi, and Panos M. Pardalos. A review of Explainable Artificial Intelligence in healthcare. Comput. Electr. Eng., 118(Part A):109370, August 2024.
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Explainable Artificial Intelligence (XAI) encompasses the strategies and methodologies used in constructing AI systems that enable end-users to comprehend and interpret the outputs and predictions made by AI models. The increasing deployment of opaque AI applications in high-stakes fields, particularly healthcare, has amplified the need for clarity and explainability. This stems from the potential high-impact consequences of erroneous AI predictions in such critical sectors. The effective integration of AI models in healthcare hinges on the capacity of these models to be both explainable and interpretable. Gaining the trust of healthcare professionals necessitates AI applications to be transparent about their decision-making processes and underlying logic. Our paper conducts a systematic review of the various facets and challenges of XAI within the healthcare realm. It aims to dissect a range of XAI methodologies and their applications in healthcare, categorizing them into six distinct groups: feature-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric approaches. Specifically, this study focuses on the significance of XAI in addressing healthcare-related challenges, underscoring its vital role in safety-critical scenarios. Our objective is to provide an exhaustive exploration of XAI's applications in healthcare, alongside an analysis of relevant experimental outcomes, thereby fostering a holistic understanding of XAI's role and potential in this critical domain.
@article{SadAli2024a,
author = "Zahra Sadeghi and Roohallah Alizadehsani and Mehmet Akif CIFCI and Samina Kausar and Rizwan Rehman and Priyakshi Mahanta and Pranjal Kumar Bora and Ammar Almasri and Rami S. Alkhawaldeh and Sadiq Hussain and Bilal Alatas and Afshin Shoeibi and Hossein Moosaei and Milan Hlad\'{\i}k and Saeid Nahavandi and Panos M. Pardalos",
title = "A review of {Explainable Artificial Intelligence} in healthcare",
journal = "Comput. Electr. Eng.",
fjournal = "Computers and Electrical Engineering",
volume = "118",
number = "Part A",
month = "August",
pages = "109370",
year = "2024",
doi = "10.1016/j.compeleceng.2024.109370",
issn = "0045-7906",
url = "https://www.sciencedirect.com/science/article/pii/S0045790624002982",
bib2html_dl_html = "https://doi.org/10.1016/j.compeleceng.2024.109370",
bib2html_dl_pdf = "https://doi.org/10.1016/j.compeleceng.2024.109370",
abstract = "Explainable Artificial Intelligence (XAI) encompasses the strategies and methodologies used in constructing AI systems that enable end-users to comprehend and interpret the outputs and predictions made by AI models. The increasing deployment of opaque AI applications in high-stakes fields, particularly healthcare, has amplified the need for clarity and explainability. This stems from the potential high-impact consequences of erroneous AI predictions in such critical sectors. The effective integration of AI models in healthcare hinges on the capacity of these models to be both explainable and interpretable. Gaining the trust of healthcare professionals necessitates AI applications to be transparent about their decision-making processes and underlying logic. Our paper conducts a systematic review of the various facets and challenges of XAI within the healthcare realm. It aims to dissect a range of XAI methodologies and their applications in healthcare, categorizing them into six distinct groups: feature-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric approaches. Specifically, this study focuses on the significance of XAI in addressing healthcare-related challenges, underscoring its vital role in safety-critical scenarios. Our objective is to provide an exhaustive exploration of XAI's applications in healthcare, alongside an analysis of relevant experimental outcomes, thereby fostering a holistic understanding of XAI's role and potential in this critical domain.",
keywords = "Explainable AI; Transparent AI; Interpretability; Healthcare",
}
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