The explainability of AI-based systems is crucial in healthcare, where these systems are increasingly used to support medical diagnoses, treatment plans, and patient outcome predictions. Methods of explainable artificial intelligence (xAI) can enable the explanation of AI decisions that can improve the understanding and trust of both practitioners and patients. This leads to more effective and ethical AI applications in healthcare. AI-based predictions in healthcare have a critical impact as they can lead to inaccurate diagnoses or treatment recommendations or even be life-threatening. xAI in healthcare refers to developing and deploying AI systems that are understandable to human users, such as healthcare practitioners and patients. However, applying xAI methods presents several challenges when applied to healthcare. These include fundamental challenges regarding the definition of an explanation in a given application and its utilities, the technical challenges of interpreting complex AI-based systems, the faithfulness of the explanations, and the trade-off between the accuracy versus the explainability of models. Additionally, the healthcare domain-specific challenges include the integration of xAI with clinical workflows, privacy concerns regarding patient data, and regulatory compliance of AI-based systems. Addressing these issues is essential for effectively developing and using AI in Healthcare.
Topics
| Interpretable diagnostic tools for medical imaging: Using xAI techniques to explain the AI’s decision-making process, aiding radiologists in understanding AI-derived diagnoses. |
| Explainable diagnostic workflows: Developing explainability for AI systems used in clinical decision support systems (CDSS) that recommend treatment plans to provide clear reasoning behind each recommendation to the medical practitioners and enhance decision-making in CDSS |
| Patient outcome prediction: Exploring the application of XAI in predictive models for patient outcomes, offering clinicians insights into how different variables influence AI systems’ prognosis in chronic diseases like diabetes. |
| Ethical AI in Patient Care: Developing xAI frameworks to ensure fairness and reduce bias in AI systems used for applications such as patient triaging and resource allocation, particularly in emergency medicine. |
| Regulatory Compliance and AI: Exploring how xAI can meet regulatory compliance by making more transparent AI decisions in drug discovery and clinical trials. |
| Enhancing Patient Engagement and Consent: Employing xAI tools with visual explanations and accessible interfaces to explain AI-based assessments and treatments to patients, fostering informed consent and patient engagement in personalised medicine. |
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