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Lu, Donna. „DeepMind's medical AI“. New Scientist 243, Nr. 3242 (August 2019): 15. http://dx.doi.org/10.1016/s0262-4079(19)31464-2.
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Konno, Masamitsu, und Hideshi Ishii. „AI for medical use“. Oncotarget 10, Nr. 2 (04.01.2019): 86–87. http://dx.doi.org/10.18632/oncotarget.26556.
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Stower, Hannah. „Transparency in medical AI“. Nature Medicine 26, Nr. 12 (Dezember 2020): 1804. http://dx.doi.org/10.1038/s41591-020-01147-y.
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Willyard, Cassandra. „Can AI Fix Medical Records?“ Nature 576, Nr. 7787 (18.12.2019): S59—S62. http://dx.doi.org/10.1038/d41586-019-03848-y.
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Price, W. Nicholson. „Distributed Governance of Medical AI“. SMU Science and Technology Law Review 25, Nr. 1 (2022): 3. http://dx.doi.org/10.25172/smustlr.25.1.2.
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Artificial intelligence (AI) has the potential to democratize expertise in medicine, bring expertise previously limited to specialists to a variety of health-care settings. But AI can easily falter, and making sure that AI works well across that variety of settings is a challenging task. Centralized governance, such as review by the Food and Drug Administration, can only do so much, since system performance will depend on the particular health-care setting and how the AI system is integrated into setting-specific clinical workflows. This Essay presents the need for distributed governance, where some oversight tasks are undertaken in localized settings. It points out the resource-based challenges of such governance and offers policy suggestions to ease the burden.
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Calisto, Francisco Maria, Carlos Santiago, Nuno Nunes und Jacinto C. Nascimento. „BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions“. Artificial Intelligence in Medicine 127 (Mai 2022): 102285. http://dx.doi.org/10.1016/j.artmed.2022.102285.
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Longoni, Chiara, Andrea Bonezzi und Carey K. Morewedge. „Resistance to Medical Artificial Intelligence“. Journal of Consumer Research 46, Nr. 4 (03.05.2019): 629–50. http://dx.doi.org/10.1093/jcr/ucz013.
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Abstract Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A–3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for consumers’ unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to consumers other than the self (study 8), or (c) that only supports, rather than replaces, a decision made by a human healthcare provider (study 9). These findings make contributions to the psychology of automation and medical decision making, and suggest interventions to increase consumer acceptance of AI in medicine.
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s, Keerthi Rani. „Artificial Intelligence on Medical Fields“. Data Analytics and Artificial Intelligence 3, Nr. 2 (01.02.2023): 113–15. http://dx.doi.org/10.46632/daai/3/2/21.
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This paper is about a overview of AI in medical field, dealing with recent and future applications that are related to AI. The aim is to develop knowledge and information about AI among the primary care physicians in the health care. Firstly, I've described about what is Artificial Intelligence then, who’s the father of it, what are the types of AI that is used in the medical field, features of AI, approaches and its needs. This paper is also about how AI is used in the health care, diagnosis, creation of new drug and delivery of drug, AI in COVID-19 pandemic, how it is used to analyze CT scans, x-rays, MRIs and about how Machine Learning is used in the health care and also how google is dealing with the future problem using Machine learning.
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Iqbal, Shazia. „Are Medical Educators Primed To Adopt Artificial Intelligence In Healthcare System And Medical Education?“ Health Professions Educator Journal 5, Nr. 1 (22.04.2022): 7–8. http://dx.doi.org/10.53708/hpej.v5i1.1707.
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Currently, the acceptance of artificial intelligence (AI) in thehealth care system and medical education is growing rapidly.It is crucial to involve healthcare professionals and medicaleducators in developing, authenticating, and implementingAI-enabled tools in medicine. A lack of AI knowledge andinadequate preparation to embrace this revolution will leadto orthodox patient care. Consequently, medical support willnot be optimal to keep pace with rapidly changing healthcaretrends word widely. This is a significant barrier to adoptingand implementing AI that will affect the quality of patient careand the future of the healthcare sector. In addition, the limitedexisting AI education programs are a barrier to the developmentand implementation of AI-assisted applications at various levelsof medical education (Charow et al., 2021).
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SAGA, Ryosuke. „AI and Visualization with Medical Data“. Journal of the Visualization Society of Japan 38, Nr. 151 (2018): 19–22. http://dx.doi.org/10.3154/jvs.38.151_19.
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Weiss, R. „New Technologies Emerge in Medical AI“. Science News 134, Nr. 7 (13.08.1988): 101. http://dx.doi.org/10.2307/3972767.
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Vinita, Kumari Pal, Singh Sonali, Sinha Anshita und Sohail Shekh Mohammad. „Medical chatbot using AI and NLP“. i-manager’s Journal on Software Engineering 16, Nr. 3 (2022): 46. http://dx.doi.org/10.26634/jse.16.3.18551.
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The use of chatbots has grown rapidly across industries, including marketing, assistive systems, education, healthcare, cultural heritage, and entertainment. This paper discusses the incentives for using chatbots and explains how useful chatbots are in various contexts. As intelligent software and hardware, also known as intelligent agents, are developed and analyzed, Artificial Intelligence (AI) is becoming more and more integrated into daily lives. From manual labor to complex procedures, intelligent agents are capable of performing a wide range of tasks. One of the simplest and most common forms of intelligent human-computer interaction is the chatbot, which is a classic example of an artificial intelligence Human-Computer Interaction (HCI) system. A chatbot is described as "a computer program designed to simulate interaction with human users, particularly over the Internet." In addition to chatbots, it also called smart bots, interactive agents, digital assistants, and intelligent conversational objects. In the midst of the COVID-19 pandemic, going to the doctor is no longer an indulgence. A chatbot is a Natural Language Processing (NLP) based chatbot to help with basic medical questions. Only the best knowledge of a chatbot can be used to answer medical questions.
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Hatherley, Joshua James. „Limits of trust in medical AI“. Journal of Medical Ethics 46, Nr. 7 (27.03.2020): 478–81. http://dx.doi.org/10.1136/medethics-2019-105935.
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Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.
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Ting, Daniel S. W., Yong Liu, Philippe Burlina, Xinxing Xu, Neil M. Bressler und Tien Y. Wong. „AI for medical imaging goes deep“. Nature Medicine 24, Nr. 5 (Mai 2018): 539–40. http://dx.doi.org/10.1038/s41591-018-0029-3.
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Sathyan, Anoop, Abraham Itzhak Weinberg und Kelly Cohen. „Interpretable AI for bio-medical applications“. Complex Engineering Systems 2, Nr. 4 (2022): 18. http://dx.doi.org/10.20517/ces.2022.41.
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This paper presents the use of two popular explainability tools called Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to explain the predictions made by a trained deep neural network. The deep neural network used in this work is trained on the UCI Breast Cancer Wisconsin dataset. The neural network is used to classify the masses found in patients as benign or malignant based on 30 features that describe the mass. LIME and SHAP are then used to explain the individual predictions made by the trained neural network model. The explanations provide further insights into the relationship between the input features and the predictions. SHAP methodology additionally provides a more holistic view of the effect of the inputs on the output predictions. The results also present the commonalities between the insights gained using LIME and SHAP. Although this paper focuses on the use of deep neural networks trained on UCI Breast Cancer Wisconsin dataset, the methodology can be applied to other neural networks and architectures trained on other applications. The deep neural network trained in this work provides a high level of accuracy. Analyzing the model using LIME and SHAP adds the much desired benefit of providing explanations for the recommendations made by the trained model.
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Hedderich, Dennis M., Matthias Keicher, Benedikt Wiestler, Martin J. Gruber, Hendrik Burwinkel, Florian Hinterwimmer, Tobias Czempiel et al. „AI for Doctors—A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging“. Healthcare 9, Nr. 10 (28.09.2021): 1278. http://dx.doi.org/10.3390/healthcare9101278.
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Successful adoption of artificial intelligence (AI) in medical imaging requires medical professionals to understand underlying principles and techniques. However, educational offerings tailored to the need of medical professionals are scarce. To fill this gap, we created the course “AI for Doctors: Medical Imaging”. An analysis of participants’ opinions on AI and self-perceived skills rated on a five-point Likert scale was conducted before and after the course. The participants’ attitude towards AI in medical imaging was very optimistic before and after the course. However, deeper knowledge of AI and the process for validating and deploying it resulted in significantly less overoptimism with respect to perceivable patient benefits through AI (p = 0.020). Self-assessed skill ratings significantly improved after the course, and the appreciation of the course content was very positive. However, we observed a substantial drop-out rate, mostly attributed to the lack of time of medical professionals. There is a high demand for educational offerings regarding AI in medical imaging among medical professionals, and better education may lead to a more realistic appreciation of clinical adoption. However, time constraints imposed by a busy clinical schedule need to be taken into account for successful education of medical professionals.
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Holzinger, Andreas T., und Heimo Muller. „Toward Human–AI Interfaces to Support Explainability and Causability in Medical AI“. Computer 54, Nr. 10 (Oktober 2021): 78–86. http://dx.doi.org/10.1109/mc.2021.3092610.
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Stanciu, Silviu, Irina Tache, Magdalena Gurzun, Alexandru Sorici, Alexandru Croitoru, Dragos Cuzino, Diana Tudor und Sorin Sorin. „Artificial Intelligence in cardiovascular medical imaging“. Romanian Journal of Military Medicine 123, Nr. 4 (01.11.2020): 310–16. http://dx.doi.org/10.55453/rjmm.2020.123.4.12.
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Artificial Intelligence (AI) has become an important tool for computer-aided diagnosis of medical imaging. This review aims to provide an overview for clinicians, explaining the relevant aspects of artificial intelligence and machine learning (ML) and presenting up-to-date applications of AI and ML techniques to medical imaging methods such as angiography, magnetic resonance, and echocardiography. For each imaging method, we present the acquisition process, the types of diagnostic test interpretation, and the challenges related to them, as well as how AI/ML techniques, have improved the process of decision making. A summary of selected works applying AI/ML techniques to medical imaging is organized into a table, which highlights the scope of the study, the dataset used, the details of each approach as well as the measured results, including objectives and criteria. The overall benefits of AI in medical imaging are extracted based on the diverse applications and high evaluation scores. In the end, cardiologists should have an advanced understanding of using AI to integrate clinical data and making the final decision in diagnosis.
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Hamamoto, Ryuji. „SS1-KL-1 APPLICATION OF AI TECHNOLOGIES FOR MEDICAL CARE“. Neuro-Oncology Advances 1, Supplement_2 (Dezember 2019): ii2. http://dx.doi.org/10.1093/noajnl/vdz039.007.
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Abstract On the basis of progress of the Machine Learning algorithm mainly on the Deep Learning, improvement of the GPU performance, the large-scale public database such as TCGA is available, big attention recently gathers in the AI technology. While large countries such as the United States or China vigorously promote AI research and development by a national policy, Cabinet Office, Government of Japan, also emphasized the importance of AI technologies in the 5th Science and Technology Basic Plan in 2016. As for the AI development, it is wrestled relatively for a long time; the word “Artificial Intelligence” was firstly used in the Dartmouth workshop in 1956. However, the AI development has not been promoted smoothly until now and repeats the active state period and the period of depression. As the current active state period of AI is called as the third AI boom, the most different point of this boom and the other booms is that AI technologies have already been involved in our social life such as the AI-based face authentication device in this period. Indeed, The US Food and Drug Administration (FDA) has already authorized around 30 AI-based medical instruments, and the Pharmaceuticals and Medical Devices Agency (PMDA) in Japan also authorized the first AI-based medical instrument last year. Therefore, now is the important time that we need to consider deeply for the creation of an affluent society, which enables coexistence of human being and AI. In this lecture, I particularly focus on medical imaging analysis using AI technologies and, would like to lecture on an action to the medical care application of the AI technology based on the experience that promoted medical AI research as the leader of two national projects relevant to medical AI called CREST and PRISM, and RIKEN AIP center.
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Mosch, Lina, Daniel Fürstenau, Jenny Brandt, Jasper Wagnitz, Sophie AI Klopfenstein, Akira-Sebastian Poncette und Felix Balzer. „The medical profession transformed by artificial intelligence: Qualitative study“. DIGITAL HEALTH 8 (Januar 2022): 205520762211439. http://dx.doi.org/10.1177/20552076221143903.
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Background Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear. Objective We analyzed physicians’ and AI experts’ stances towards AI-induced changes. This concerned (1) physicians’ tasks, (2) job replacement risk, and (3) implications for the ways of working, including human–AI interaction, changes in job profiles, and hierarchical and cross-professional collaboration patterns. Methods We adopted an exploratory, qualitative research approach, using semi-structured interviews with 24 experts in the fields of AI and medicine, medical informatics, digital medicine, and medical education and training. Thematic analysis of the interview transcripts was performed. Results Specialized tasks currently performed by physicians in all areas of medicine would likely be taken over by AI, including bureaucratic tasks, clinical decision support, and research. However, the concern that physicians will be replaced by an AI system is unfounded, according to experts; AI systems today would be designed only for a specific use case and could not replace the human factor in the patient–physician relationship. Nevertheless, the job profile and professional role of physicians would be transformed as a result of new forms of human–AI collaboration and shifts to higher-value activities. AI could spur novel, more interprofessional teams in medical practice and research and, eventually, democratization and de-hierarchization. Conclusions The study highlights changes in job profiles of physicians and outlines demands for new categories of medical professionals considering AI-induced changes of work. Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. There is a need for the development of scenarios and concepts for future job profiles in the health professions as well as their education and training.
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Wood, Elena A., Brittany L. Ange und D. Douglas Miller. „Are We Ready to Integrate Artificial Intelligence Literacy into Medical School Curriculum: Students and Faculty Survey“. Journal of Medical Education and Curricular Development 8 (Januar 2021): 238212052110240. http://dx.doi.org/10.1177/23821205211024078.
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Background: The effects of Artificial Intelligence (AI) technology applications are already felt in healthcare in general and in the practice of medicine in the disciplines of radiology, pathology, ophthalmology, and oncology. The expanding interface between digital data science, emerging AI technologies and healthcare is creating a demand for AI technology literacy in health professions. Objective: To assess medical student and faculty attitudes toward AI, in preparation for teaching AI foundations and data science applications in clinical practice in an integrated medical education curriculum. Methods: An online 15-question semi-structured survey was distributed among medical students and faculty. The questionnaire consisted of 3 parts: participant’s background, AI awareness, and attitudes toward AI applications in medicine. Results: A total of 121 medical students and 52 clinical faculty completed the survey. Only 30% of students and 50% of faculty responded that they were aware of AI topics in medicine. The majority of students (72%) and faculty (59%) learned about AI from the media. Faculty were more likely to report that they did not have a basic understanding of AI technologies (χ2, P = .031). Students were more interested in AI in patient care training, while faculty were more interested in AI in teaching training (χ2, P = .001). Additionally, students and faculty reported comparable attitudes toward AI, limited AI literacy and time constraints in the curriculum. There is interest in broad and deep AI topics. Our findings in medical learners and teaching faculty parallel other published professional groups’ AI survey results. Conclusions: The survey conclusively proved interest among medical students and faculty in AI technology in general, and in its applications in healthcare and medicine. The study was conducted at a single institution. This survey serves as a foundation for other medical schools interested in developing a collaborative programming approach to address AI literacy in medical education.
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AlZaabi, Adhari, Saleh AlMaskari und Abdulrahman AalAbdulsalam. „Are physicians and medical students ready for artificial intelligence applications in healthcare?“ DIGITAL HEALTH 9 (Januar 2023): 205520762311521. http://dx.doi.org/10.1177/20552076231152167.
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Background Artificial intelligence (AI) Healthcare applications are listed in the national visions of some Gulf Cooperation Council countries. A successful use of AI depends on the attitude and perception of medical experts of its applications. Objective To evaluate physicians and medical students’ attitude and perception on AI applications in healthcare. Method A web-based survey was disseminated by email to physicians and medical students. Results A total of 293 (82 physicians and 211 medical students) individuals have participated (response rate is 27%). Seven participants (9%) reported knowing nothing about AI, while 208 (69%) were aware that it is an emerging field and would like to learn about it. Concerns about AI impact on physicians’ employability were not prominent. Instead, the majority (n=159) agreed that new positions will be created and the job market for those who embrace AI will increase. They reported willingness to adapt AI in practice if it was incorporated in international guidelines (30.5%), published in respected scientific journals (17.1%), or included in formal training (12.2%). Almost two of the three participants agreed that dedicated courses will help them to implement AI. The most commonly reported problem of AI is its inability to provide opinions in unexpected scenarios. Half of the participants think that both the manufacturer and physicians should be legally liable for medical errors occur due to AI-based decision support tools while more than one-third (36.77%) think that physicians who make the final decision should be legally liable. Senior physicians were found to be less familiar with AI and more concerned about physicians’ legal liability in case of a medical error. Conclusion Physicians and medical students showed positive attitudes and willingness to learn about AI applications in healthcare. Introducing AI learning objectives or short courses in medical curriculum would help to equip physicians with the needed skills for AI-augmented healthcare system.
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Sumit. „AI Health Care Chatbot“. International Journal for Modern Trends in Science and Technology 6, Nr. 12 (13.12.2020): 219–24. http://dx.doi.org/10.46501/ijmtst061241.
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Healthcare bot is a technology that makes interaction between man and machine possible by using Artificial Intelligence with the support of dialog flow. Now a day people tend to seek knowledge or information from internet that concern with health through online healthcare services. To lead a good life healthcare is very much important. But it is very difficult to obtain the consultation with the doctor in case of any health issues. The basic aim of this system is to bridge the vocabulary gap between the doctors by giving self-diagnosis from the comfort of one’s place. The proposed idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. To reduce the healthcare costs and improve accessibility to medical knowledge the medical bot is built. Certain bots act as a medical reference books, which helps the patient know more about their disease and helps to improve their health. The user can achieve the real benefit of a bot only when it can diagnose all kind of disease and provide necessary information. Hence, people will have an idea about their health and have the right protection.
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Pashkov, Vitalii M., Andrii O. Harkusha und Yevheniia O. Harkusha. „ARTIFICIAL INTELLIGENCE IN MEDICAL PRACTICE: REGULATIVE ISSUES AND PERSPECTIVES“. Wiadomości Lekarskie 73, Nr. 12 (2020): 2722–27. http://dx.doi.org/10.36740/wlek202012204.
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The aim of the research is to identify specific of AI in healthcare, its nature, and specifics and to establish complexities of AI implementation in healthcare and to propose ways to eliminate them. Materials and methods: This study was conducted during June-October of 2020. Through a broad literature review, analysis of EU, USA regulation acts, scientific researches and opinions of progressive-minded people in this sphere this paper provide a guide to understanding the essence of AI in healthcare and specifics of its regulation. It is based on dialectical, comparative, analytic, synthetic and comprehensive methods. Results: One of the first broad definitions of AI sounded like “Artificial Intelligence is the study of ideas which enable computers to do the things that make people seem intelligent ... The central goals of Artificial Intelligence are to make computers more useful and to understand the principles which make intelligence possible.” There are two approaches to name this technology - “Artificial intelligence” and “Augmented Intelligence.” We prefer to use a more common category of “Artificial intelligence” rather than “Augmented Intelligence” because the last one, from our point of view, leaves much space for “human supervision” meaning, and that will limit the sense of AI while it will undoubtedly develop in future. AI in current practice is interpreted in three forms, they are: AI as a simple electronic tool without any level of autonomy (like electronic assistant, “calculator”), AI as an entity ith some level of autonomy, but under human control, and AI as an entity with broad autonomy, substituting human's activity wholly or partly, and we have to admit that the first one cannot be considered as AI at all in current conditions of science development. Description of AI often tends to operate with big technological products like DeepMind (by Google), Watson Health (by IBM), Healthcare's Edison (by General Electric), but in fact, a lot of smaller technologies also use AI in the healthcare field – smartphone applications, wearable health devices and other examples of the Internet of Things. At the current stage of development AI in medical practice is existing in three technical forms: software, hardware, and mixed forms using three main scientific-statistical approaches – flowchart method, database method, and decision-making method. All of them are useable, but they are differently suiting for AI implementation. The main issues of AI implementation in healthcare are connected with the nature of technology in itself, complexities of legal support in terms of safety and efficiency, privacy, ethical and liability concerns. Conclusion: The conducted analysis makes it possible to admit a number of pros and cons in the field of AI using in healthcare. Undoubtedly this is a promising area with a lot of gaps and grey zones to fill in. Furthermore, the main challenge is not on technology itself, which is rapidly growing, evolving, and uncovering new areas of its use, but rather on the legal framework that is clearly lacking appropriate regulations and some political, ethical, and financial transformations. Thus, the core questions regarding is this technology by its nature is suitable for healthcare at all? Is the current legislative framework looking appropriate to regulate AI in terms of safety, efficiency, premarket, and postmarked monitoring? How the model of liability with connection to AI technology using in healthcare should be constructed? How to ensure privacy without the restriction of AI technology use? Should intellectual privacy rights prevail over public health concerns? Many questions to address in order to move in line with technology development and to get the benefits of its practical implementation.
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Lillehaug, Svein-Ivar, und Susanne P. Lajoie. „AI in medical education—another grand challenge for medical informatics“. Artificial Intelligence in Medicine 12, Nr. 3 (März 1998): 197–225. http://dx.doi.org/10.1016/s0933-3657(97)00054-7.
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Muller, Heimo, Michaela Theresia Mayrhofer, Evert-Ben Van Veen und Andreas Holzinger. „The Ten Commandments of Ethical Medical AI“. Computer 54, Nr. 7 (Juli 2021): 119–23. http://dx.doi.org/10.1109/mc.2021.3074263.
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El Naqa, Issam, John M. Boone, Stanley H. Benedict, Mitchell M. Goodsitt, Heang‐Ping Chan, Karen Drukker, Lubomir Hadjiiski, Dan Ruan und Berkman Sahiner. „AI in medical physics: guidelines for publication“. Medical Physics 48, Nr. 9 (September 2021): 4711–14. http://dx.doi.org/10.1002/mp.15170.
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M Gassner, U., und U. Juknat. „Regulatory Approaches to AI in Medical Practice“. European Pharmaceutical Law Review 3, Nr. 4 (2019): 176–83. http://dx.doi.org/10.21552/eplr/2019/4/8.
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Di Nucci, Ezio. „Should we be afraid of medical AI?“ Journal of Medical Ethics 45, Nr. 8 (21.06.2019): 556–58. http://dx.doi.org/10.1136/medethics-2018-105281.
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I analyse an argument according to which medical artificial intelligence (AI) represents a threat to patient autonomy—recently put forward by Rosalind McDougall in the Journal of Medical Ethics. The argument takes the case of IBM Watson for Oncology to argue that such technologies risk disregarding the individual values and wishes of patients. I find three problems with this argument: (1) it confuses AI with machine learning; (2) it misses machine learning’s potential for personalised medicine through big data; (3) it fails to distinguish between evidence-based advice and decision-making within healthcare. I conclude that how much and which tasks we should delegate to machine learning and other technologies within healthcare and beyond is indeed a crucial question of our time, but in order to answer it, we must be careful in analysing and properly distinguish between the different systems and different delegated tasks.
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Uchino, Eiichiro, Yoshinori Tamada und Yasushi Okuno. „AI for Nephrology Using Electronic Medical Records“. Proceedings for Annual Meeting of The Japanese Pharmacological Society 93 (2020): 2—CS—2. http://dx.doi.org/10.1254/jpssuppl.93.0_2-cs-2.
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Smith, Maxwell J., und Sally Bean. „AI and Ethics in Medical Radiation Sciences“. Journal of Medical Imaging and Radiation Sciences 50, Nr. 4 (Dezember 2019): S24—S26. http://dx.doi.org/10.1016/j.jmir.2019.08.005.
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Avanzo, Michele, Annalisa Trianni, Francesca Botta, Cinzia Talamonti, Michele Stasi und Mauro Iori. „Artificial Intelligence and the Medical Physicist: Welcome to the Machine“. Applied Sciences 11, Nr. 4 (13.02.2021): 1691. http://dx.doi.org/10.3390/app11041691.
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Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
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Hamamoto, Ryuji, Kruthi Suvarna, Masayoshi Yamada, Kazuma Kobayashi, Norio Shinkai, Mototaka Miyake, Masamichi Takahashi et al. „Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine“. Cancers 12, Nr. 12 (26.11.2020): 3532. http://dx.doi.org/10.3390/cancers12123532.
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In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.
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Tang, Xiaoli. „The role of artificial intelligence in medical imaging research“. BJR|Open 2, Nr. 1 (November 2020): 20190031. http://dx.doi.org/10.1259/bjro.20190031.
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Without doubt, artificial intelligence (AI) is the most discussed topic today in medical imaging research, both in diagnostic and therapeutic. For diagnostic imaging alone, the number of publications on AI has increased from about 100–150 per year in 2007–2008 to 1000–1100 per year in 2017–2018. Researchers have applied AI to automatically recognizing complex patterns in imaging data and providing quantitative assessments of radiographic characteristics. In radiation oncology, AI has been applied on different image modalities that are used at different stages of the treatment. i.e. tumor delineation and treatment assessment. Radiomics, the extraction of a large number of image features from radiation images with a high-throughput approach, is one of the most popular research topics today in medical imaging research. AI is the essential boosting power of processing massive number of medical images and therefore uncovers disease characteristics that fail to be appreciated by the naked eyes. The objectives of this paper are to review the history of AI in medical imaging research, the current role, the challenges need to be resolved before AI can be adopted widely in the clinic, and the potential future.
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Gao, Shuqing, Lingnan He, Yue Chen, Dan Li und Kaisheng Lai. „Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media“. Journal of Medical Internet Research 22, Nr. 7 (13.07.2020): e16649. http://dx.doi.org/10.2196/16649.
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Background High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public’s views on medical AI. Currently, the opinions of the general public on this matter remain unclear. Objective The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors. Methods Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis. Results Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors. Conclusions Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients’ emotional needs instead of focusing merely on technical issues.
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Xiao, Yineng. „Application of Neural Network Algorithm in Medical Artificial Intelligence Product Development“. Computational and Mathematical Methods in Medicine 2022 (08.06.2022): 1–11. http://dx.doi.org/10.1155/2022/5413202.
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With the continuous deepening of artificial intelligence (AI) in the medical field, the social risks brought by the development and application of medical AI products have become increasingly prominent, bringing hidden worries to the protection of civil rights, social stability, and healthy development. There are many new problems that need to be solved in our country’s existing risk regulation theories when dealing with such risks. By introducing the theory of risk administrative law, it analyzes the social risks of medical AI, organically combines the principle of risk prevention with benefit measurement, and systematically and flexibly reconstructs the theoretical system of medical AI social risk assessment. This paper has completed the following work: (1) reviewed and sorted out the works and papers related to medical AI ethics, medical AI risk, etc., and sorted out the current situation of medical AI social risk regulation at home and abroad to provide help for follow-up research. (2) The related technologies of artificial neural network (ANN) are introduced, and the risk assessment index system of medical AI is constructed. (3) With the self-designed dataset, the trained neural network model is utilized to assess risk. The experimental results reveal that the created BPNN model’s error is relatively tiny, indicating that the algorithm model developed in this research is worth popularizing and applying.
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Wiedeman, Christopher, Huidong Xie, Xuanqin Mou und Ge Wang. „Innovating the Medical Imaging Course“. Technology & Innovation 21, Nr. 4 (01.12.2020): 1–11. http://dx.doi.org/10.21300/21.4.2020.5.
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Artificial intelligence (AI) and machine learning (ML), especially deep learning, have generated tremendous impacts throughout our society, including the tomographic medical imaging field. In contrast to computer vision and image analysis, which have been major application examples of deep learning and deal with existing images, tomographic medical imaging mainly produces cross-sectional or volumetric images of internal structures from sensor measurements. Recently, deep learning has started being actively developed worldwide for medical imaging, including both tomographic reconstruction and image analysis. While medical imaging is a well-established field, in which extensive teaching experience has been accumulated over the past few decades, updating the medical imaging course to reflect AI/ML influence is a new challenge given the rapidly changing landscape of AI-based medical imaging, particularly deep tomographic imaging. In the 2019 fall semester, the medical imaging course at Rensselaer Polytechnic Institute was modified to include an AI framework with positive feedback from students. Encouragingly, many students showed a strong motivation to learn AI in classes and hands-on projects, as confirmed in their survey reports. In the 2020 fall semester, we improved this course further, incorporating new advances. This article describes our teaching philosophy, practice, and considerations with respect to integrating deep learning, tomographic imaging, and hands-on programming to redefine the medical imaging course. Furthermore, given the persistent pandemic, online teaching and examination have become an integral part of higher education. These needs will be addressed as well, with the hope of developing an open course in the future.
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Wu, Chenxi, Huiqiong Xu, Dingxi Bai, Xinyu Chen, Jing Gao und Xiaolian Jiang. „Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis“. BMJ Open 13, Nr. 1 (Januar 2023): e066322. http://dx.doi.org/10.1136/bmjopen-2022-066322.
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ObjectivesMedical artificial intelligence (AI) has been used widely applied in clinical field due to its convenience and innovation. However, several policy and regulatory issues such as credibility, sharing of responsibility and ethics have raised concerns in the use of AI. It is therefore necessary to understand the general public’s views on medical AI. Here, a meta-synthesis was conducted to analyse and summarise the public’s understanding of the application of AI in the healthcare field, to provide recommendations for future use and management of AI in medical practice.DesignThis was a meta-synthesis of qualitative studies.MethodA search was performed on the following databases to identify studies published in English and Chinese: MEDLINE, CINAHL, Web of science, Cochrane library, Embase, PsycINFO, CNKI, Wanfang and VIP. The search was conducted from database inception to 25 December 2021. The meta-aggregation approach of JBI was used to summarise findings from qualitative studies, focusing on the public’s perception of the application of AI in healthcare.ResultsOf the 5128 studies screened, 12 met the inclusion criteria, hence were incorporated into analysis. Three synthesised findings were used as the basis of our conclusions, including advantages of medical AI from the public’s perspective, ethical and legal concerns about medical AI from the public’s perspective, and public suggestions on the application of AI in medical field.ConclusionResults showed that the public acknowledges the unique advantages and convenience of medical AI. Meanwhile, several concerns about the application of medical AI were observed, most of which involve ethical and legal issues. The standard application and reasonable supervision of medical AI is key to ensuring its effective utilisation. Based on the public’s perspective, this analysis provides insights and suggestions for health managers on how to implement and apply medical AI smoothly, while ensuring safety in healthcare practice.PROSPERO registration numberCRD42022315033.
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Frank, Darius-Aurel, Christian T. Elbæk, Caroline Kjær Børsting, Panagiotis Mitkidis, Tobias Otterbring und Sylvie Borau. „Drivers and social implications of Artificial Intelligence adoption in healthcare during the COVID-19 pandemic“. PLOS ONE 16, Nr. 11 (22.11.2021): e0259928. http://dx.doi.org/10.1371/journal.pone.0259928.
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The COVID-19 pandemic continues to impact people worldwide–steadily depleting scarce resources in healthcare. Medical Artificial Intelligence (AI) promises a much-needed relief but only if the technology gets adopted at scale. The present research investigates people’s intention to adopt medical AI as well as the drivers of this adoption in a representative study of two European countries (Denmark and France, N = 1068) during the initial phase of the COVID-19 pandemic. Results reveal AI aversion; only 1 of 10 individuals choose medical AI over human physicians in a hypothetical triage-phase of COVID-19 pre-hospital entrance. Key predictors of medical AI adoption are people’s trust in medical AI and, to a lesser extent, the trait of open-mindedness. More importantly, our results reveal that mistrust and perceived uniqueness neglect from human physicians, as well as a lack of social belonging significantly increase people’s medical AI adoption. These results suggest that for medical AI to be widely adopted, people may need to express less confidence in human physicians and to even feel disconnected from humanity. We discuss the social implications of these findings and propose that successful medical AI adoption policy should focus on trust building measures–without eroding trust in human physicians.
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Dhawan, Shiven. „Employability of the Machine Learning Algorithms in the Early Detection and Diagnosis of Multiple Diseases“. International Journal of Research in Medical Sciences and Technology 13, Nr. 01 (2022): 142–51. http://dx.doi.org/10.37648/ijrmst.v13i01.013.
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The disease area models intend to bring the clinical and artificial intelligence (AI) fields together so that individuals can figure out how well AI and medication can cooperate. A few specialists have recently utilized AI-based ways to create independent disease detection systems. Early infection distinguishing proof might assist with reducing the number of individuals. To more readily comprehend the job of Artificial knowledge in the clinical field, we plan to lead a far-reaching concentrate on AI applications for the medical services area. To start, we'll audit the features and intentions for involving AI in the medical care industry. From top to bottom, we go over AI-based analyses for incorporating AI also the medical care area. Then, we initially go over AI's technical issues in the clinical industry and afterwards show how AI can help. We likewise investigate the effect of AI in the clinical field. Besides, we present a few eminent drives showing the significance of AI in medical care applications and administrations. At last, examine a few issues in disease distinguishing proof and recommend future innovative work regions that will prompt the utilization of AI in the medical services area.
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Park, Seong Ho, Kyung-Hyun Do, Sungwon Kim, Joo Hyun Park und Young-Suk Lim. „What should medical students know about artificial intelligence in medicine?“ Journal of Educational Evaluation for Health Professions 16 (03.07.2019): 18. http://dx.doi.org/10.3352/jeehp.2019.16.18.
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Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.
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Schmidt Nordmo, Tor-Arne, Ove Kvalsvik, Svein Ove Kvalsund, Birte Hansen und Michael A. Riegler. „Fish AI“. Nordic Machine Intelligence 2, Nr. 2 (02.06.2022): 1–3. http://dx.doi.org/10.5617/nmi.9657.
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Sustainable Commercial Fishing is the second challenge at the Nordic AI Meet following the successful MedAI, which had a focus on medical image segmentation and transparency in machine learning (ML)-based systems. FishAI focuses on a new domain, namely, commercial fishing and how to make it more sustainable with the help of machine learning. A range of public available datasets is used to tackle three specific tasks. The first one is to predict fishing coordinates to optimize catching of specific fish, the second one is to create a report that can be used by experienced fishermen, and the third task is to make a sustainable fishing plan that provides a route for a week. The second and third task require to some extend explainable and interpretable models that can provide explanations. A development dataset is provided and all methods will be tested on a concealed test dataset and assessed by an expert jury.
artificial intelligence; machine learning; segmentation; transparency; medicine
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Paranjape, Ketan, Michiel Schinkel, Rishi Nannan Panday, Josip Car und Prabath Nanayakkara. „Introducing Artificial Intelligence Training in Medical Education“. JMIR Medical Education 5, Nr. 2 (03.12.2019): e16048. http://dx.doi.org/10.2196/16048.
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Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.
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Khairatun Hisan, Urfa, und Muhammad Miftahul Amri. „Artificial Intelligence for Human Life: A Critical Opinion from Medical Bioethics Perspective – Part I“. Journal of Public Health Sciences 1, Nr. 02 (11.12.2022): 100–111. http://dx.doi.org/10.56741/jphs.v1i02.214.
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Since the end of the second artificial intelligence (AI) winter in 1993, AI popularity has been skyrocketing. Catalyzed by the rapid advancements of supporting technologies such as computational power and storage capacity, AI has been widely developed for various sectors of human lives. Although there is a common global consensus stating that the development of AI should consider ethical principles, until when this paper was written, the debates on how we can define an AI as ‘ethical’, how the AI ethical guidelines should be formulated, who has the rights to determine the ethical aspects of AI, and how can we reinforce the guidelines to the AI developer/operator is still going on. In this article, we summarized the research and studies related to AI development from the ethical perspective that have been conducted within the last 10 years. We discussed several cases of AI development misconduct and ‘unethical’ AI, such as biased algorithms and privacy breaches during data gathering.
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Sapci, A. Hasan, und H. Aylin Sapci. „Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review“. JMIR Medical Education 6, Nr. 1 (30.06.2020): e19285. http://dx.doi.org/10.2196/19285.
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Background The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. Objective The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. Methods A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. Results This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. Conclusions To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.
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Burney, Ikram A., und Nisar Ahmad. „Artificial Intelligence in Medical Education: A citation-based systematic literature review“. Journal of Shifa Tameer-e-Millat University 5, Nr. 1 (03.09.2022): 43–53. http://dx.doi.org/10.32593/jstmu/vol5.iss1.183.
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Purpose: This review aims to describe the existing and emerging role of Artificial intelligence (AI) in medical education, as this may help set future directions.
Methodology: Articles on AI in medical education describing integration of AI or machine-learning (ML) in undergraduate medical curricula or structured postgraduate residency programs were extracted from SCOPUS database. The paper followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) research methodology. Articles describing AI or ML, but not directly related to teaching and training in structured programs were excluded.
Results: Of the 1020 documents published till October 15, 2020, 218 articles are included in the final analysis. A sharp increase in the number of published articles was observed 2018 onwards. Articles describing surgical skills training, case-based reasoning, physicians' role in the evolving scenario, and the attitudes of medical students towards AI in radiology were cited frequently. Of the 50 top-cited papers, 16 (32%) were ‘commentary’ articles, 13 (26%) review articles, 13 (26%) articles correlated usefulness of ML and AI with human performance, whereas 8 (16%) assessed the perceptions of students toward the integration of AI in medical practice.
Conclusion: AI should be taught in medical curricula to prepare doctors for tomorrow, and at the same time, could be used for teaching, assessment, and providing feedback in various disciplines.
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Stepaniuk, Ruslan, Mikhailo Shcherbakovskyi, Vasyl Kikinchuk, Iryna Petrova und Vadym Babakin. „Problems of investigation of medical crimes in Ukraine“. Revista Amazonia Investiga 11, Nr. 57 (08.11.2022): 39–47. http://dx.doi.org/10.34069/ai/2022.57.09.4.
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In the article, the authors identified the main reasons for the low level of investigation of medical crimes in Ukraine based on the analysis of 78 criminal cases of medical malpractice considered by courts since 2013, the study of statistical information, scientific literature on the problems of investigating medical crimes, national legislation. It was concluded there are significant problems with the investigation of medical crimes in Ukraine. Less than one percent of the number of initiated criminal cases is sent to court. Most of the cases sent to court end with a guilty verdict, however, medical workers are released from real deprivation or restriction of freedom for various reasons. According to the specialties of medical workers, the most criminogenic are obstetrics and gynecology, surgery, anesthesiology, and emergency care for injuries and internal diseases. Methods of committing medical crimes are associated with using incorrect methods of providing medical care and with the untimely or incorrect diagnosis of the disease. The problems of investigating medical crimes in Ukraine are due to a number of reasons, including the closeness of the results of the post-mortem examination of the corpse to relatives of the deceased, the lack of independent forensic medical examination institutions in Ukraine, and gaps in the legal regulation of the protection of medical records from unauthorized access. This greatly complicates the establishment of a causal relationship between the actions or inaction of medical workers and the negative consequences that have occurred.
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Price II, William Nicholson. „Distributed Governance of Medical AI“. SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4051834.
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Eliot, Lance. „Interoperability Of AI-to-AI Multi-Lawyering“. SSRN Electronic Journal, 2021. http://dx.doi.org/10.2139/ssrn.3990307.
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Landi, Giovanni. „The Ethics of AI vs Ethical AI“. SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3916915.
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