ETHICAL AND REGULATORY COMPLIANCE IN THE EVALUATION OF CLINICAL RESEARCH INVOLVING ARTIFICIAL INTELLIGENCE IN SUB SAHARAN AFRICA

In recent years, the use of artificial intelligence (AI) in research involving human participants has gained popularity in clinical and biomedical research domains, more so in advance economies than in low medium income countries (LMIC) like Cameroon. AI is transformative in its ability to perform faster repetitive tasks, analyze large amounts of data, and potentially leading to enhance clinical research decision-making. As with all research involving human participants, research using AI should be in compliance with ethical principles and subject to regulatory guidelines for better protection of volunteering research participants. The use of AI in research involving human participants presents new ethical and regulatory challenges, including those related to privacy and data confidentiality, transparency, amplification of bias, and implications for maintaining human autonomy and oversight. Existing guidelines of most research ethics committees relating to digital health technology evaluation for human research are not up-to- date to address AI evaluation in clinical research. In view of providing a regulatory compliant structure that protects the human participant’s privacy and mitigation of bias Sub-Saharan African countries have an obligation to update their standard operating procedures to integrate aspects for evaluating AI research. There are however, a few existing guidelines from developed countries like the US and Canada that provide the ethical foundations for the use of AI in clinical research. Tools are still required to enhance the protocol review process by the research ethics committees charged with ensuring consistency, efficiency and sustainability across institutions, to reflect accepted standards of ethical research review oversight. This document aims to focus on AI contributions in clinical research, challenges and adopted guidelines for evaluation of AI-driven clinical research. This is in view of providing support for Institutional Review Boards (IRBs) and other ethics advocacy institutions with the awareness and practical friendly user steps to identify, assess, and mitigate potential risks to participants in an AI-driven research, during the ethical review process.