Explainable Artificial Intelligence (XAI) in healthcare: Interpretable Models for Clinical Decision Support

Abstract

In healthcare, the incorporation of Artificial Intelligence (AI) plays a pivotal role in enhancing diagnostic precision and guiding treatment decisions. Nevertheless, the lack of transparency in conventional AI models poses challenges in gaining the trust of clinicians and comprehending the rationale behind their decisions. This research paper explores Explainable Artificial Intelligence (XAI) and its application in healthcare, with a specific focus on transparent models designed for clinical decision support in various medical disciplines. The paper initiates by underscoring the crucial requirement for transparency and interpretability in AI systems within the healthcare realm. Recognizing the diverse nature of medical specialties, the study investigates tailored XAI approaches to meet the distinctive needs of areas such as radiology, pathology, cardiology, and oncology. Through a thorough review of existing literature and analysis, the research identifies key obstacles and prospects in implementing XAI across varied medical contexts. In the field of radiology, a cornerstone in diagnostic imaging, XAI proves beneficial by elucidating the decision-making procedures behind image analysis algorithms. The research probes into the impact of interpretable models on radiological diagnoses, examining how clinicians can seamlessly integrate AI-generated insights into their decision-making workflows. Within pathology, where precision is of utmost importance, the paper clarifies how XAI can enhance transparency in histopathological assessments. By demystifying the intricacies of AI-driven pathology models, the study aims to empower pathologists to leverage these tools for more accurate diagnoses. Cardiology, characterized by a complex interplay of physiological parameters, benefits from XAI by offering clinicians intelligible explanations for cardiovascular risk predictions and treatment recommendations. The research delves into the interpretability of AI models in cardiology, highlighting their potential to enhance clinical decision support systems. Moreover, in the field of oncology, where treatment decisions hinge on precise identification and characterization of tumors, the paper explores how XAI aids in unraveling intricate machine learning models. This, in turn, fosters trust among oncologists in utilizing AI for personalized treatment strategies.

Note:
Funding declaration: No funding was received.

Conflict of Interests: No conflict of interest

Keywords: Explainable Artificial Intelligence, Healthcare, Public health, Medicine, Explainable AI, Machine Learning, Deep Learning