Special Session 3 - Medical Professionalism and Ethics I

Advancement in artificial intelligence (AI) technologies is set to confront society, alter our ways of practice, and disrupt long-standing notions and boundaries of professional responsibility and accountability in healthcare. In response, the Professionalism and Ethics Committee under the Hong Kong Academy of Medicine established a “Task Force on AI” to look into concomitant ethical and legal implications with a view to promoting the safe, effective, and responsible adoption of these technologies. Here, the author will map out some of the pertinent issues, from the impact of AI on the traditional doctor-patient relationship and regulatory framework to medico-legal liabilities, research, equity, and distributive justice. The emphasis is on exploring how postgraduate training, our standard of practice, and public policy may evolve to help navigate this future landscape of enormous promises and challenges.

Big data science involving machine learning, deep Learning and artificial intelligence has revolutionised research in various disciplines, including medicine. These advancements have significantly contributed to predicting, preventing, and treating diseases more effectively by leveraging data-driven approaches. Big data science enable large-scale and multi-dimensional aggregation and analysis of heterogeneous health data sources. However, along with these advancements, ethical challenges arise. One of the primary concerns is the risk of compromising privacy and personal autonomy. With the aggregation and analysis of large amounts of health data, there is a potential for unauthorized access or unintended identification of individuals. Safeguarding patient confidentiality becomes paramount in ensuring the ethical viability of health-related big data studies. Deidentification techniques play a crucial role in protecting patient privacy by removing or anonymizing identifiable information. Transparency, trust, and fairness are also important ethical considerations in the use of big data. The public's demand for transparency in data usage and the assurance of fair and unbiased analysis should be addressed to maintain public trust in these technologies.


Data ownership is a complex matter in big data science, particularly when considering health-related data. Determining who owns the data and how it can be used ethically is a topic that requires careful consideration. Group-level ethical harms should also be taken into account. While big data analysis can provide valuable insights and benefits at the population level, there is a risk of inadvertently causing harm or perpetuating biases against certain groups. Attention should be given to ensuring that the use of big data does not disproportionately impact vulnerable populations or reinforce existing inequalities. Another ethical consideration is the distinction between academic and commercial uses of big data. Academic research often prioritizes knowledge generation and societal benefits, while commercial uses focus on profit and market-driven goals. Balancing the interests and potential conflicts between these two domains is crucial to maintain ethical integrity in the use of big data. In conclusions, it is essential to address the ethical challenges associated with health-related big data science. Safeguarding patient confidentiality, ensuring privacy, promoting transparency and fairness, considering group-level ethical harms, and navigating the distinction between academic and commercial uses are all critical aspects of ensuring the ethical viability of health-related big data studies.

A clinician-scientist, or more commonly referred to as a "physician-scientist" in the US, is a person who has a clinical degree, most often an MD degree or equivalent, and who devotes a considerable amount of his/her time to research. Clinician-scientists may or may not have an additional degree, such as Master's or PhD degree. Clinician-scientists function in various settings, with many of them in academia but also many in the private sector including the pharmaceutical, biotechnology, healthcare, and financial community. The clinician-scientist "phenotype" incorporates clinical care, research, and education. It also incorporates and ethos of "bedside to bench" in a quest to take clinical problems and find solutions in academia and industry which can then be brought back to the care of patients. The clinician-scientist develops many core competencies and contributes to the healthcare system in many ways: evidence-based practice, critical thinking and professional development, interdisciplinary collaborations, innovation, new therapeutic approaches, direct application of research findings, attraction and education of innovators and future leaders, healthcare policy, and funding priorities. It is in the economic best interest of the healthcare system to invest in clinician-scientists who are instrumental in: new drug development, intellectual property, patents, licenses royalties, funding, partnerships, cost savings, attraction of patients and new faculty, spin-off companies and new jobs. Clinician-scientists are trained to uphold and advance ethical standards in the healthcare system. As an example, we will discuss the important ethical issues involved in the rapidly developing field of Xenotransplantation.


Despite the important roles of clinician scientists in a healthcare ecosystem that is rapidly expanding worldwide, many countries are having trouble with reduced interest in research among physicians and this group is often referred to as an "endangered species." The increase in the length of time to training, uncertain research funding and financial insecurity are some of many factors contributing to this growing crisis. To sustain a robust growing clinician-scientist community we have to provide opportunities and encouragement all along the educational path. We must provide: 1) more financial support during training; 2) mentorship; 3) diverse research opportunities; 4) work-life balance; 5) flexible curricula; 6) ability to sustain research interests during clinical training; 7) career development opportunities with financial security; 8) loan forgiveness. We will summarize examples of programs to encourage clinician-scientist development in Nephrology at various stages of their development and including a call for the world to take a lesson from the global nature of science to bring understanding and tolerance to our planet.