AI, Brain Death Detection, and Islamic Law

The integration of machine learning systems for detecting covert consciousness in neurologically injured patients presents a complex challenge spanning clinical medicine, AI ethics, and Islamic jurisprudence. This research proposes addressing the shift from binary clinical verdicts to probabilistic neural-state estimates by leveraging three foundational constructs within Islamic legal epistemology: clear evidentiary proof (bayyina), epistemic certainty (yaqin), and theological agnosticism regarding the soul. The study surveys AI-based consciousness detection literature, aligns it with Islamic brain death scholarship, and identifies critical challenges, discussing its implications.

Why it matters

This development is strategically important as it highlights the intersection of advanced technology with deeply held ethical and religious frameworks, particularly in critical medical decisions. It necessitates the development of robust governance structures and interdisciplinary dialogues to ensure that AI applications are both medically sound and culturally/religiously acceptable, impacting public trust and regulatory landscapes.

Key insights

• Machine learning systems are being developed to detect covert consciousness in neurologically injured patients.
• The deployment of such systems creates a profound challenge at the intersection of clinical medicine, AI ethics, and Islamic jurisprudence.
• AI systems are shifting clinical verdicts from binary outcomes to probabilistic, temporally granular neural-state estimates.
• Islamic legal epistemology offers three foundational constructs—bayyina, yaqin, and theological agnosticism about the soul—to address this shift.
• The research surveys technical literature on AI-based consciousness detection and maps it against Islamic brain death scholarship.
• Key challenges are identified at the convergence of AI, medical diagnosis, and religious legal frameworks.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.16903