O-052 USING COMPUTER ASSISTED DETECTION TO PROMOTE HEALTH EQUITY THROUGH HIGH-VOLUME IDENTIFICATION OF SILICOSIS AND TUBERCULOSIS IN EX-MINERS: A BIO-ETHICS APPROACH

Abstract Introduction In Southern Africa, a powerful legacy of social injustice has been the prevalence of occupational lung disease, particularly silicosis and tuberculosis (TB) in those who worked in South Africa’s gold mines. Although Artificial Intelligence (AI) is being increasingly applied for healthcare purposes, distrust about introducing “disruptive” technologies persists. Intrinsic and contextual factors also influence where and how such innovations are initiated. These require careful scrutiny to ensure that health equity is promoted. Methods We describe and appraise an AI application currently being developed, specifically the use of computer assisted detection (CAD) for TB and/or silicosis on chest x-rays, to support more efficient and equitable adjudication of compensation claims from former miners in southern Africa. Using a bio-ethical lens that considers the principles of beneficence, non-maleficence, autonomy and justice and adds explicability as a core principle, this study focuses on the apprehensions of users and stakeholders. Results Issues of concern include funding a sustainable health service delivery model in which CAD can be incorporated, CAD accuracy, possible biases in training of CAD systems, data privacy, impact on human skill development, transparency and accountability in CAD use, as well as intellectual property ownership. Discussion This paper discusses ways in which each of these potential obstacles to successful use of CAD could be mitigated. Conclusion From the outset, efforts to overcome technical implementation challenges must be considered to ensure ethical use. It is timely to take stock of barriers that might undermine the advancement of AI innovation on behalf of those who have been socially marginalized.