Introduction to the Issue on "Artificial Intelligences"

Therapies, 2018

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AI and Society-the encompassing subject of this journal-is probably illustrated (although not exhausted) by interrogations specifically focused on AI in medicine. Justified by spectacular achievements recently facilitated through applied deep learning methodology (based on neural networks), the "Everything is possible" view dominates this new hour in the "boom and bust" curve of AI performance history. The optimistic view collides head on with the "It's not possible," ascertainments often originating in a skewed understanding of both AI and medicine. The meaning of the conflicting views-whose score reality will eventually settle-can be assessed only by addressing the nature of medicine. Specifically, by answering the question: Which part of medicine, if any, can and should be entrusted to AI-now or at some moment in the future? That both medicine and AI will continue to change goes without saying. Medicine is focused on what is needed to maintain life. It is an endeavor within the larger context of social organization of productive activity, of economic and political interaction, of culture. These and the environment, which used to be acknowledged as the dominant factor before the genetics revolution, affect medicine in all its aspects. AI in medicine is the shorthand for the meeting point between a new technology and what healthcare practitioners are or should be. Specifically:-How to identify the specific talent and dedication healthcare demands-How medical education should be conceived and carried out

Yearbook of medical informatics, 2019

The history of early biomedical computing, including the first AI approaches, can be seen as a series of attempts to investigate, understand, and build computational models of the scientific knowledge and problem solving heuristics used by biomedical scientists, while also developing and testing computa-Summary Background: The rise of biomedical expert heuristic knowledgebased approaches for computational modeling and problem solving, for scientific inquiry and medical decision-making, and for consultation in the 1970's led to a major change in the paradigm that affected all of artificial intelligence (AI) research. Since then, AI has evolved, surviving several "winters", as it has oscillated between relying on expensive and hard-to-validate knowledge-based approaches, and the alternative of using machine learning methods for inferring classification rules from labelled datasets. In the past couple of decades, we are seeing a gradual but progressive intertwining of the two. Objectives: To give an overview of early directions in AI in medicine and threads of some subsequent developments motivated by the very different goals of scientific inquiry for biomedical research, and for computational modeling of clinical reasoning and more general healthcare problem solving from the perspective of today's "AI-Deep Learning Boom". To show how, from the beginning, AI was central to Biomedical and Health Informatics (BMHI), as a field investigating how to understand intelligent thinking in dealing professionally with the practice for healthcare, developing mathematical models, technology, and software tools to aid human experts in biomedicine, despite many previous bouts of "exuberant optimism" about the methodologies deployed. Methods: An overview and commentary on some of the early research and publications in AI in biomedicine, emphasizing the different approaches to the modeling of problems involved in clinical practice in contrast to those of biomedical science. A concluding reflection of a few current challenges and pitfalls of AI in some biomedical applications. Conclusion: While biomedical knowledge-based systems played a critical role in influencing AI in its early days, 50 years later they have taken a back seat behind "Deep Learning" which promises to discover knowledge structures for inference and prediction, both in science and for clinical decision-support. Early work on AI for medical consultation turned out to be more useful for explanation and teaching than for clinical practice, as had been originally intended. Today, despite the many reported successes of deep learning, fundamental scientific challenges arise in drawing on models of brain science, cognition, and language, if AI is to augment and complement rather than replace human judgment and expertise in biomedicine while also incorporating these advances for translational medicine. Understanding clinical phenotypes and how they relate to precision and personalization of care requires not only scientific inquiry, but also humanistic models of treatment that respond to patient and practitioner narrative exchanges, since it is the stories and insights of human experts which encourage what Norbert Weiner termed the ethical "human use of human beings", so central to adherence to the Hippocratic Oath

IMCSI, 2019

The AI in Medicine project began with a simple yet complex and multilevel question. In late 2017, prompted by direct experience of researching human-machine interchanges, we asked whether the traditional principles of interaction between a physician and a patient in the time of technological and computer revolution had changed. That, in turn, led to other questions. Was the very concept of principles of doctor-patient interaction, as an interaction between 'Subject' and 'Object', still relevant in the 21st century? While such principles are not deterministic, in the past they were followed meticulously. Whether they still wield their original instructive power is an intriguing question. But it is not our immediate purpose. We do not intend to replace one set of principles, locked up to time and place, with another set equally constrained. We acknowledge that there would be no quick and easy answers. As an initial move we simply seek to elicit the right questions. We hope our paper will offer a mechanism for constructive engagement, discussion and discovery. The broadest possible engagement is crucial to meeting the kaleidoscope of irregular issues in interactions between medical professionals and general public that characterizes our time of Internet dominance. More importantly, the paper extends an invitation to think anew, across the traditional barriers of scholarly disciplines, policies and habits.

2019

Scientists are adept at engaging in debate about the nuances of the theories and methods with one another, but they are less proficient at explaining the general value and practical importance of their research to other scholars who are not trained in their specific disciplines let alone to public at large and policymakers. However, recent shifts in professional scholarly forums indicate the critical importance of effective communication about specific scientific research to wider scholarly community. AI in Medicine is a domain marked by hot discussions in professional literature, media, and public in large. As a part WMSCI/IMSCI 2019 Interdisciplinary Communication Program this paper is based on the research piece we have presented at the IMSCI conference, Orlando 2019. The presentation has been summarized in eleven theses and propositions.