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      Artificial intelligence meets medical robotics

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          Abstract

          Artificial intelligence (AI) applications in medical robots are bringing a new era to medicine. Advanced medical robots can perform diagnostic and surgical procedures, aid rehabilitation, and provide symbiotic prosthetics to replace limbs. The technology used in these devices, including computer vision, medical image analysis, haptics, navigation, precise manipulation, and machine learning (ML) , could allow autonomous robots to carry out diagnostic imaging, remote surgery, surgical subtasks, or even entire surgical procedures. Moreover, AI in rehabilitation devices and advanced prosthetics can provide individualized support, as well as improved functionality and mobility (see the figure). The combination of extraordinary advances in robotics, medicine, materials science, and computing could bring safer, more efficient, and more widely available patient care in the future. –Gemma K. Alderton

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          Biomedical applications of soft robotics

          Matteo Cianchetti, Cecilia Laschi, Arianna Menciassi … (2018)
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            Resistance to Medical Artificial Intelligence

            Chiara Longoni, Andrea Bonezzi, Carey K. Morewedge (2019)
            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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              Preparing Medical Imaging Data for Machine Learning

              Martin Willemink, Wojciech A. Koszek, Cailin Hardell … (2020)
              Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability. Supervised artificial intelligence (AI) methods for evaluation of medical images require a curation process for data to optimally train, validate, and test algorithms. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations).
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                Author and article information

                Journal
                Title: Science
                Abbreviated Title: Science
                Publisher: American Association for the Advancement of Science (AAAS)
                ISSN (Print): 0036-8075
                ISSN (Electronic): 1095-9203
                Publication date Created: July 14 2023
                Publication date (Print): July 14 2023
                Volume: 381
                Issue: 6654
                Pages: 141-146
                Affiliations
                [1 ]Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
                [2 ]Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
                [3 ]Department of Industrial Engineering and Operations Research and Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.
                [4 ]School of Engineering and Materials Science, Queen Mary University of London, London, UK.
                [5 ]The BioRobotics Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà, Pisa, Italy.
                [6 ]Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
                [7 ]Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA.
                [8 ]Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
                [9 ]John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
                [10 ]Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA.
                [11 ]Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
                Article
                DOI: 10.1126/science.adj3312
                PubMed ID: 37440630
                SO-VID: 7f7acb2a-f923-4ba4-9e1d-11b1a35da631
                Copyright © © 2023
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