In their article,1 Jecker et al highlight a widespread and hotly debated issue in the current application of artificial intelligence (AI) in medicine: whether we should develop more powerful AI. There are many perspectives on this question. I would like to address it from the perspective of the fundamental purpose of medicine. Since its inception, medicine has been dedicated to alleviating human suffering and ensuring health equity. For thousands of years, we have made great efforts and conducted many investigations to (...) eliminate human suffering and have achieved remarkable results. However, the reality of achieving health equity falls far short of expectations. The WHO considers health equity to be a fundamental human right, meaning that there should be no unjustified, avoidable or remediable differences among the population as a whole, whether those differences result from social, economic, demographic or geographic factors, or from other forms of inequality such as gender, race, disability or sexual orientation.2 Health equity means reducing and ultimately eliminating disparities in health outcomes between vulnerable groups and others, caused by health risk factors and difficulties in accessing quality health services. This means that we must strive to reduce structural social bias. It is well known that the traditional healthcare system is riddled with various health inequities. …. (shrink)

In Sahan et al ’s article,1 they present the ethical challenges faced by clinical laboratory scientists in genetic medicine, including labour allocation and responsibility, interpretation and accuracy of results with new technologies, and the need for better standardisation and ethical consistency. At the same time, they also propose a potential solution to the aforementioned challenges: ethical preparedness(EP). Along with their vivid case discussions and insightful analysis, I would like to propose two more points that are worth further examination and discussion (...) to aid stakeholders in better comprehending the ethical issues that clinical laboratory scientists face in the practice of genetic medicine. The case 1 reported by Sahan et al raises concerns about who should be responsible for tracking families or individuals with genetic results, particularly in the absence of mainstream services to track future risks. However, this should not be a factor influencing the decision to inform gene carriers. If there is a clear division of responsibilities in current practice, clinical laboratory scientists can report detected gene carriers without psychological burden. However, the decision to report gene carriers should be based on who is being served when clinical laboratory scientists face genetic problems in clinical practice and who is being considered when …. (shrink)

Artificial intelligence (AI), represented by machine learning, artificial neural networks and deep learning, is impacting all areas of medicine, including translational research (from bench to bedside to health policy), clinical medicine (including diagnosis, treatment, prognosis and healthcare resource allocation) and public health. At a time when almost everyone is focused on how to better realise the promise of AI to transform the entire healthcare system, Dr Appel calls for public attention to the AI in medicine and the negative outcome penalty (...) paradox. Proposing this topic has deepened our thinking about the application of AI in clinical diagnostics, and also prompted us to find more effective ways to integrate AI more effectively into future clinical practice. In addition to Dr Appel’s insightful advice, I hope to offer three other possible perspectives, including changing public perceptions, re-engineering clinical practice processes and introducing more stakeholders, to further the discussion on this topic. (shrink)