DEVELOPING SHARIAH ACCOUNTABILITY FRAMEWORK FOR ARTIFICIAL INTELLIGENCE IN HEALTHCARE USING THE FUZZY DELPHI METHOD
Authors
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Ahmad Safwan Yunos
Academy of Islamic Civilisation, University of Technology Malaysia, Johor Bahru, Johor, Malaysia.
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Muhammad Danial Hakimi Nor Shaifulizam
Faculty of Literature and Human Science, Mohammed V University, Rabat, Morocco.
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Mohammad Naqib Hamdan
Academy of Islamic Civilisation, University of Technology Malaysia, Johor Bahru, Johor, Malaysia.
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Zahin Mohamad Tahir
Academy of Islamic Civilisation, University of Technology Malaysia, Johor Bahru, Johor, Malaysia.
DOI:
https://doi.org/10.33102/jfatwa.vol31no3.822Keywords:
Artificial Intelligence, Healthcare, Shariah, Accountability, Fuzzy Delphi.Abstract
Artificial intelligence (AI) in healthcare has transformed clinical decision-making, diagnostics and service delivery, while simultaneously raising complex ethical, legal and accountability challenges. From an Islamic perspective, these challenges are particularly significant, as AI-assisted medical decisions involve human life, dignity and moral responsibility. Existing AI ethical frameworks largely adopt secular principles and do not sufficiently address Shariah-based concepts such as accountability, ḍamān (liability) and maqāṣid al-sharīʿah. This study aims to develop a Shariah Accountability Framework for Artificial Intelligence in Healthcare (SAFAIH) through expert consensus. Employing the Fuzzy Delphi Method (FDM), data were collected from multidisciplinary experts in Shariah, artificial intelligence and healthcare ethics. A seven-point fuzzy linguistic scale was used to evaluate proposed accountability elements, with acceptance determined by threshold values (d ≤ 0.2), expert consensus (≥ 75%) and defuzzification scores (≥ 0.5). The findings validated nine core elements of Shariah-compliant AI accountability: Allah SWT’s Authority, Human Control over AI, AI as a Tool, Developer Responsibility, Doctor Responsibility, Hospital Responsibility, Staff Responsibility, Liability and Compensation, and AI Limitations with Collective Responsibility. Developer Responsibility and Doctor Responsibility emerged as the highest-priority elements. The study concludes that SAFAIH provides a robust, ethically grounded framework that integrates theological principles, professional accountability and institutional responsibility. This framework offers practical guidance for policymakers, healthcare institutions, AI developer and practitioners to ensure that AI deployment in healthcare remains ethically and aligned with Islamic values.
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