Explainable AI in Oral Carcinoma Detection: Legal Challenges and Policy Pathways in India

Abstract

Oral carcinoma is a disproportionate burden on India’s public health system, causing almost one-third of the world’s oral cancer incidents, mainly due to tobacco usage, inequity in the socio-economic structure, and late presentation. Artificial Intelligence (AI) provides new avenues for early identification, mainly in resource-scarce environments, but the “black-box” behavior of most AI models undermines their clinical credibility. Explainable AI (XAI), which focuses on transparency, understanding, and responsibility, becomes a necessity in healthcare and not merely a luxury. The current paper investigates the potential role of AI and XAI in the identification of oral carcinoma in the Indian setting. It looks at epidemiological patterns, gaps in detection, and new AI programs, prior to the critical discussion of the legal and ethical issues concerning the protection of data, liability, intellectual property right, and the vacuum in regulations. comparative points are derived from the U.S. FDA’s Good Machine Learning Practices and the EU AI Act. Based in Beauchamp and Childress’ principles in bioethics, the current paper contends that India needs a convergent legal–policy structure in order for XAI in oncology to be safe, inclusive, and equitable.

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