Three Epochs of Artificial Intelligence in Health Care.

Importance: Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities. Observations: While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people's daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model's behavior. Prompts such as "Write this note for a specialist consultant" and "Write this note for the patient's mother" will produce markedly different content. Conclusions and Relevance: Foundation models and generative AI represent a major revolution in AI's capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

Chat GPT: origen, evolución, retos e impactos en la educación / Chat GPT: Origen, Evolution, Challenges and Impacts on Education

Introducción: Los avances de unas tecnologías y la obsolescencia de otras marchan a una velocidad inimaginable, especialmente en este siglo xxi. En los últimos meses de 2022 y primeros meses de 2023 muchas incógnitas y controversias en diferentes campos han surgido en torno a los Chat GPS, una innovación que presenta desafíos nunca pensados para la sociedad actual, así como nuevos retos que impactarán de manera directa en la formación y/o desempeño de profesores, estudiantes, profesionales de la salud, juristas, políticos, informáticos, bibliotecarios, científicos y cualquier ciudadano. Objetivo: Identificar algunas características del chat GPT y su posible impacto en el educación. Posicionamiento de los autores: Se leen en las noticias y reportajes valoraciones de especialistas; se han realizado encuentros virtuales y exposiciones; y están disponibles diversos artículos y videos sobre este tema, algunos llegan a ser elaborados con el propio asistente. Por la novedad del tema, la reciente incorporación como herramienta para el desarrollo profesional, así como por el interés mostrado en los últimos días por la comunidad de profesores de las ciencias médicas cubanas, y considerando que esta herramienta es resultado del desarrollo de la inteligencia artificial, cabe preguntarse: ¿en qué consiste? y ¿cuáles son sus perspectivas? Conclusiones: Resulta oportuno acercarse al tema desde las posibilidades y los retos que abre a la educación y el aprendizaje, en particular a la docencia médica(AU)

Introduction: The advances of some technologies and the obsolescence of others are marching at an unimaginable speed, especially in this twenty-first century. In the last months of 2022 and first months of 2023, many questions and controversies in different fields have arisen with respect to Chat GPT, an innovation that presents challenges never thought of before for today's society, as well as new challenges that will have a direct impact on the training and/or performance of professors, students, health professionals, law practitioners, politicians, computer scientists, librarians, scientists and any citizen. Objective: To identify some technological characteristics of Chat GPT. Positioning of the authors: In news and reports, assessments by specialists are read; virtual meetings and presentations have been held; and several articles and videos on this topic are available, some of them even elaborated by the assistant itself. Due to the novelty of the subject, its recent assimilation as a tool for professional development, as well as the interest shown in recent days by the community of professors of Cuban medical sciences and considering that this tool is the result of the development of artificial intelligence, it is worth wondering what it consists in and what its prospects are. Conclusions: It is appropriate to approach the subject with a focus on the possibilities and challenges that it opens to education and learning (AU)

Current Trends in Artificial Intelligence Application for Endourology and Robotic Surgery.

With the advent of electronic medical records and digitalization of health care over the past 2 decades, artificial intelligence (AI) has emerged as an enabling tool to manage complex datasets and deliver streamlined data-driven patient care. AI algorithms have the ability to extract meaningful signal from complex datasets through an iterative process akin to human learning. Through advancements over the past decade in deep learning, AI-driven innovations have accelerated applications in health care. Herein, the authors explore the development of these emerging AI technologies, focusing on the application of AI to endourology and robotic surgery.

[A brief history of artificial intelligence]. / Une brève introduction à l'intelligence artificielle.

For more than a decade, we have witnessed an acceleration in the development and the adoption of artificial intelligence (AI) technologies. In medicine, it impacts clinical and fundamental research, hospital practices, medical examinations, hospital care or logistics. These in turn contribute to improvements in diagnostics and prognostics, and to improvements in personalised and targeted medicine, advanced observation and analysis technologies, or surgery and other assistance robots. Many challenges in AI and medicine, such as data digitalisation, medical data privacy, algorithm explicability, inclusive AI system development or their reproducibility, have to be tackled in order to build the confidence of medical practitioners in these technologies. This will be possible by mastering the key concepts via a brief history of artificial intelligence.

TITLE: Une brève introduction à l'intelligence artificielle. ABSTRACT: Depuis plus d'une décennie, l'intelligence artificielle (IA) vit une accélération dans son développement et son adoption. En médecine, elle intervient dans la recherche fondamentale et clinique, la pratique hospitalière, les examens médicaux, les soins ou encore la logistique. Ce qui contribue à l'affinement des diagnostics et des pronostics, à une médecine encore plus personnalisée et ciblée, à des avancées dans les technologies d'observations et d'analyses ou encore dans les outils d'interventions chirurgicales et autres robots d'assistance. De nombreux enjeux propres à l'IA et à la médecine, tels que la dématérialisation des données, le respect de la vie privée, l'explicabilité1 des algorithmes, la conception de systèmes d'IA inclusifs ou leur reproductibilité, sont à surmonter pour construire une confiance du corps hospitalier dans ces outils. Cela passe par une maîtrise des concepts fondamentaux que nous présentons ici.

[Explore the history of artificial intelligence, to better define and understand it]. / Parcourir l'histoire de l'intelligence artificielle, pour mieux la définir et la comprendre.

TITLE: Parcourir l'histoire de l'intelligence artificielle, pour mieux la définir et la comprendre. ABSTRACT: L'intelligence artificielle est une expression fourre-tout, qui suscite autant d'espoirs que de craintes. Cette locution a envahi les médias, les conférences, les conversations, mais aussi les appels à projets des institutions de recherche et de diverses associations. On ne peut quasiment plus élaborer de projet de recherche sans mentionner une interface avec l'intelligence artificielle. Dans cet article, après la présentation d'une brève vision historique, nous proposerons une définition de l'intelligence artificielle et un paysage des possibles offerts par celle-ci.

A brief history of artificial intelligence and robotic surgery in orthopedics & traumatology and future expectations.

Recently, the rate of the production and renewal of information makes it almost impossible to be updated. It is quite difficult to process and interpret large amounts of data by human beings. Unlimited memory capacities, learning abilities, artificial intelligence (AI) applications, and robotic surgery techniques cause orthopedic surgeons to be concerned about losing their jobs. The idea of AI, which was first introduced in 1956, has evolved over time by revealing deep learning and evolutionary plexus that can mimic the human neuron cell. Image processing is the leading improvement in developed algorithms. Theoretically, these algorithms appear to be quite successful in interpreting medical images and orthopedic decision support systems for preoperative evaluation. Robotic surgeons have emerged as significant competitors in carrying out the taken decisions. The first robotic applications of orthopedic surgery started in 1992 with the ROBODOC system. Applications started with hip arthroplasty continued with knee arthroplasty. Publications indicate that problems such as blood loss and infection caused by the long operation time in the early stages have been overcome in time with the help of learning systems. Comparative studies conducted with humans indicate that robots are better than humans in providing limb lengthening, patient satisfaction, and cost. As in all new technologies, the developments in both AI applications and robotics surgery indicate that technology is in favor in terms of cost/benefit analyses. Although studies indicate that new technologies are more successful than humans, the replacement of technology with experience and long-term results with traditional methods will not be observed in the near future.

Digital technology and social change: the digital transformation of society from a historical perspective
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Digital technology, including its omnipresent connectedness and its powerful artificial intelligence, is the most recent long wave of humanity's socioeconomic evolution. The first technological revolutions go all the way back to the Stone, Bronze, and Iron Ages, when the transformation of material was the driving force in the Schumpeterian process of creative destruction. A second metaparadigm of societal modernization was dedicated to the transformation of energy (aka the "industrial revolutions"), including water, steam, electric, and combustion power. The current metaparadigm focuses on the transformation of information. Less than 1% of the world's technologically stored information was in digital format in the late 1980s, surpassing more than 99% by 2012. Every 2.5 to 3 years, humanity is able to store more information than since the beginning of civilization. The current age focuses on algorithms that automate the conversion of data into actionable knowledge. This article reviews the underlying theoretical framework and some accompanying data from the perspective of innovation theory.
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La tecnología digital, que incluye una altísima conectividad y una poderosa inteligencia artificial, constituye el desarrollo más reciente y significativo en la evolución socioeconómica de la humanidad. Las primeras revoluciones tecnológicas se remontan a las Edades de Piedra, Bronce y Hierro, cuando la transformación del material fue la fuerza impulsora en el proceso Schumpeteriano de destrucción creativa. Un segundo metaparadigma de modernización social fue el que ocurrió con la transformación de la energía (también conocida como "revoluciones industriales"), incluyendo el agua, el vapor, la electricidad y la energía de combustión. El metaparadigma actual se centra en la transformación de la información. A fines de la década de 1980, menos del 1% de la información almacenada tecnológicamente en el mundo estaba en formato digital y ha llegado a más del 99% en 2012. Cada 2,5 a 3 años, la humanidad puede almacenar más información que desde el comienzo de la civilización. La era actual se centra en algoritmos que automatizan la conversión de datos en conocimiento procesable. Desde la perspectiva de la teoría de la innovación, este artículo revisa el marco teórico subyacente y algunos datos inherentes a él.

La technologie numérique, sa connectivité omniprésente et la puissance de l'intelligence artificielle font vivre à l'humanité sa phase d'évolution la plus longue sur un plan socio-économique. Les premières révolutions technologiques remontent à l'âge de pierre, du bronze et du fer, lorsque la transformation de la matière était le moteur du processus schumpétérien de destruction créatrice. La transformation de l'énergie qu'elle soit hydraulique, à vapeur, électrique ou par combustion (aussi appelée "révolutions industrielles") est à l'origine d'un deuxième méta-modèle de modernisation sociétale basée sur le changement technologique. La transformation de l'information est au centre du méta-modèle actuel. Moins de 1 % de l'information était stockée en format numérique à la fin des années 80, contre plus de 99 % en 2012Tous les 2,5 à 3 ans, l'humanité est capable d'archiver plus d'informations que celles créées depuis le début des civilisations. Nous sommes maintenant entrés dans l'ère des algorithmes qui automatisent la conversion des données en connaissances exploitables. Dans cet article, nous nous plaçons du point de vue de l'innovation pour analyser le cadre théorique de cette transformation et certaines données qui y sont inhérentes.

Intelligent, Autonomous Machines in Surgery.

Surgeons perform two primary tasks: operating and engaging patients and caregivers in shared decision-making. Human dexterity and decision-making are biologically limited. Intelligent, autonomous machines have the potential to augment or replace surgeons. Rather than regarding this possibility with denial, ire, or indifference, surgeons should understand and steer these technologies. Closer examination of surgical innovations and lessons learned from the automotive industry can inform this process. Innovations in minimally invasive surgery and surgical decision-making follow classic S-shaped curves with three phases: (1) introduction of a new technology, (2) achievement of a performance advantage relative to existing standards, and (3) arrival at a performance plateau, followed by replacement with an innovation featuring greater machine autonomy and less human influence. There is currently no level I evidence demonstrating improved patient outcomes using intelligent, autonomous machines for performing operations or surgical decision-making tasks. History suggests that if such evidence emerges and if the machines are cost effective, then they will augment or replace humans, initially for simple, common, rote tasks under close human supervision and later for complex tasks with minimal human supervision. This process poses ethical challenges in assigning liability for errors, matching decisions to patient values, and displacing human workers, but may allow surgeons to spend less time gathering and analyzing data and more time interacting with patients and tending to urgent, critical-and potentially more valuable-aspects of patient care. Surgeons should steer these technologies toward optimal patient care and net social benefit using the uniquely human traits of creativity, altruism, and moral deliberation.

Machine Learning in Drug Discovery and Development Part 1: A Primer.

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.

Mechanizing medicine - Tomorrows history started yesterday.

History is by nature a retrospective subject, there usually being an interval between any event, a review or impact of the subject being considered. This NPSA Historian's paper, takes a long and quick historical view of influences that fostered changes resulting in the current state of affairs in the field of medicine and medical care. The fields of medicine and surgery, are undergoing rapid changes as a result of technological and other advances that are making tomorrow's medical history seemingly happening yesterday. Prospectively, the impact of current change and its rapidity has the potential to radically change the future practice of the art and craft of our profession.

The role of artificial intelligence and machine learning in predicting orthopaedic outcomes.

This annotation briefly reviews the history of artificial intelligence and machine learning in health care and orthopaedics, and considers the role it will have in the future, particularly with reference to statistical analyses involving large datasets. Cite this article: Bone Joint J 2019;101-B:1476-1478.

Admiring the Great Mountain: A Celebration Special Issue in Honor of Stephen Grossberg's 80th Birthday.

This editorial summarizes selected key contributions of Prof. Stephen Grossberg and describes the papers in this 80th birthday special issue in his honor. His productivity, creativity, and vision would each be enough to mark a scientist of the first caliber. In combination, they have resulted in contributions that have changed the entire discipline of neural networks. Grossberg has been tremendously influential in engineering, dynamical systems, and artificial intelligence as well. Indeed, he has been one of the most important mentors and role models in my career, and has done so with extraordinary generosity and encouragement. All authors in this special issue have taken great pleasure in hereby commemorating his extraordinary career and contributions.

Artificial intelligence: Implications for the future of work.

Artificial intelligence (AI) is a broad transdisciplinary field with roots in logic, statistics, cognitive psychology, decision theory, neuroscience, linguistics, cybernetics, and computer engineering. The modern field of AI began at a small summer workshop at Dartmouth College in 1956. Since then, AI applications made possible by machine learning (ML), an AI subdiscipline, include Internet searches, e-commerce sites, goods and services recommender systems, image and speech recognition, sensor technologies, robotic devices, and cognitive decision support systems (DSSs). As more applications are integrated into everyday life, AI is predicted to have a globally transformative influence on economic and social structures similar to the effect that other general-purpose technologies, such as steam engines, railroads, electricity, electronics, and the Internet, have had. Novel AI applications in the workplace of the future raise important issues for occupational safety and health. This commentary reviews the origins of AI, use of ML methods, and emerging AI applications embedded in physical objects like sensor technologies, robotic devices, or operationalized in intelligent DSSs. Selected implications on the future of work arising from the use of AI applications, including job displacement from automation and management of human-machine interactions, are also reviewed. Engaging in strategic foresight about AI workplace applications will shift occupational research and practice from a reactive posture to a proactive one. Understanding the possibilities and challenges of AI for the future of work will help mitigate the unfavorable effects of AI on worker safety, health, and well-being.

Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art - with Reflections on Present AIM Challenges.

BACKGROUND: The rise of biomedical expert heuristic knowledge-based 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.

Artificial Intelligence in Medicine: Weighing the Accomplishments, Hype, and Promise.

INTRODUCTION: Artificial Intelligence in Medicine (AIM) research is now 50 years old, having made great progress that has tracked the corresponding evolution of computer science, hardware technology, communications, and biomedicine. Characterized as being in its "adolescence" at an international meeting in 1991, and as "coming of age" at another meeting in 2007, the AIM field is now more visible and influential than ever before, paralleling the enthusiasm and accomplishments of artificial intelligence (AI) more generally. OBJECTIVES: This article summarizes some of that AIM history, providing an update on the status of the field as it enters its second half-century. It acknowledges the failure of AI, including AIM, to live up to early predictions of its likely capabilities and impact. METHODS: The paper reviews and assesses the early history of the AIM field, referring to the conclusions of papers based on the meetings in 1991 and 2007, and analyzing the subsequent evolution of AIM. CONCLUSION: We must be cautious in assessing the speed at which further progress will be made, despite today's wild predictions in the press and large investments by industry, including in health care. The inherent complexity of medicine and of clinical care necessitates that we address issues of usability, workflow, transparency, safety, and formal clinical trials. These requirements contribute to an ongoing research agenda that means academic AIM research will continue to be vibrant while having new opportunities for more interactions with industry.