A vision for sustainable, equitable healthcare applications with XAI and transfer learning to augment neural models

This paper presents a vision to propel approaches for sustainability and thus equity in healthcare applications with XAI (explainable AI) and transfer learning to augment deep learning neural models, e.g. multimodal ones (combining text, images and heterogeneous data). Transfer learning can promote scalability, i.e. hypotheses learned on one illness can be transferred to related settings. XAI solutions build trust and transparency in clinical decision-making (trace causes of errors, and map empirical observations to theories with factual reasoning etc.) As stated by real-life clinical practitioners: "Explainability is crucial to aid doctors to ensure that techniques work, and traceability helps improve solutions". It can thus benefit human-AI collaboration in medicine to enhance accessibility. The vision in this paper aims to make positive impacts on United Nations Sustainable Development Goals, e.g. Goal 3 on Good Health and Well-Being. It can play a key role in data analytics for real-life applications, especially by helping to advance global health and equity.