Ethics Framework for Generative AI in Healthcare Software
Monash University
Primary Supervisor: Dr Chetan Arora
Co-supervised with Prof. John Grundy · Dr Tanjila Kanij · Dr Anuradha Madugalla
Research Project
Yutan's research investigates the ethical challenges associated with generative AI applications in healthcare — including data privacy, algorithmic bias, transparency, and accountability — all of which influence both the reliability of AI systems and public trust in those systems.
The project collects and analyses insights from healthcare professionals, researchers, and software engineers to develop a comprehensive ethical framework ensuring responsible implementation of AI technologies in healthcare software. By grounding the work in the lived experience of practitioners, the framework is designed to be both theoretically rigorous and practically actionable.
This work sits at the intersection of software engineering, bioethics, and responsible AI — contributing to an emerging body of knowledge on how to build and deploy generative AI systems in settings where the stakes for individuals and communities are highest.
Publications
Huang, Yutan, Chetan Arora, Wen Cheng Huong, Tanjila Kanij, Anuradha Madugalla, and John Grundy. Applied Soft Computing (2026): 114789. A comprehensive systematic mapping study that identifies and categorises ethical concerns arising from generative AI systems, alongside concrete mitigation strategies drawn from the literature and practitioner perspectives.
Yutan Huang, Tanjila Kanij, Anuradha Madugalla, Shruti Mahajan, Chetan Arora, John Grundy. ENASE 2024. Explores how generative AI can enable adaptive user experiences that respond to individual user needs, preferences, and contexts in software applications.
Huang, Yutan. ICSE 2024 Companion. Presents a methodology for using AI assistants to generate personalised user experiences informed by user personas, demonstrating the potential of generative AI to support user-centred design in software engineering.