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      Medical Expectations of Physicians on AI Solutions in Daily Practice: Cross-Sectional Survey Study

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          Abstract

          Background

          The use of artificial intelligence (AI) in medicine has been a trending subject in the past few years. Although not frequently used in daily practice yet, it brings along many expectations, doubts, and fears for physicians. Surveys can be used to help understand this situation.

          Objective

          This study aimed to explore the degree of knowledge, expectations, and fears on possible AI use by physicians in daily practice, according to sex and time since graduation.

          Methods

          An electronic survey was sent to physicians of a large hospital in Brazil, from August to September 2022.

          Results

          A total of 164 physicians responded to our survey. Overall, 54.3% (89/164) of physicians considered themselves to have an intermediate knowledge of AI, and 78.5% (128/163) believed that AI should be regulated by a governmental agency. If AI solutions were reliable, fast, and available, 77.9% (127/163) intended to frequently or always use AI for diagnosis (143/164, 87.2%), management (140/164, 85.4%), or exams interpretation (150/164, 91.5%), but their approvals for AI when used by other health professionals (85/163, 52.1%) or directly by patients (82/162, 50.6%) were not as high. The main benefit would be increasing the speed for diagnosis and management (106/163, 61.3%), and the worst issue would be to over rely on AI and lose medical skills (118/163, 72.4%). Physicians believed that AI would be useful (106/163, 65%), facilitate their work (140/153, 91.5%), not alter the number of appointments (80/162, 49.4%), not interfere in their financial gain (94/162, 58%), and not replace their jobs but be an additional source of information (104/162, 64.2%). In case of disagreement between AI and physicians, most (108/159, 67.9%) answered that a third opinion should be requested. Physicians with ≤10 years since graduation would adopt AI solutions more frequently than those with >20 years since graduation ( P=.04), and female physicians were more receptive to other hospital staff using AI than male physicians ( P=.008).

          Conclusions

          Physicians were shown to have good expectations regarding the use of AI in medicine when they apply it themselves, but not when used by others. They also intend to use it, as long as it was approved by a regulatory agency. Although there was hope for a beneficial impact of AI on health care, it also brings specific concerns.

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          Most cited references20

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          A Consensus-Based Checklist for Reporting of Survey Studies (CROSS).

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            Artificial Intelligence in Health Care: Bibliometric Analysis

            Background As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. Methods The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. Results The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. Conclusions This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.
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              Patient Perspectives on the Use of Artificial Intelligence for Skin Cancer Screening: A Qualitative Study

              How do patients perceive the use of artificial intelligence for skin cancer screening? A qualitative study conducted at the Brigham and Women’s Hospital and the Dana-Farber Cancer Institute evaluated 48 patients, 33% with a history of melanoma, 33% with a history of nonmelanoma skin cancer only, and 33% with no history of skin cancer. While 75% of the patients stated that they would recommend artificial intelligence to friends and family members, 94% expressed the importance of symbiosis between humans and artificial intelligence. Patients appear to be receptive to the use of artificial intelligence for skin cancer screening if the integrity of the human physician-patient relationship is preserved. The use of artificial intelligence (AI) is expanding throughout the field of medicine. In dermatology, researchers are evaluating the potential for direct-to-patient and clinician decision-support AI tools to classify skin lesions. Although AI is poised to change how patients engage in health care, patient perspectives remain poorly understood. To explore how patients conceptualize AI and perceive the use of AI for skin cancer screening. A qualitative study using a grounded theory approach to semistructured interview analysis was conducted in general dermatology clinics at the Brigham and Women’s Hospital and melanoma clinics at the Dana-Farber Cancer Institute. Forty-eight patients were enrolled. Each interview was independently coded by 2 researchers with interrater reliability measurement; reconciled codes were used to assess code frequency. The study was conducted from May 6 to July 8, 2019. Artificial intelligence concept, perceived benefits and risks of AI, strengths and weaknesses of AI, AI implementation, response to conflict between human and AI clinical decision-making, and recommendation for or against AI. Of 48 patients enrolled, 26 participants (54%) were women; mean (SD) age was 53.3 (21.7) years. Sixteen patients (33%) had a history of melanoma, 16 patients (33%) had a history of nonmelanoma skin cancer only, and 16 patients (33%) had no history of skin cancer. Twenty-four patients were interviewed about a direct-to-patient AI tool and 24 patients were interviewed about a clinician decision-support AI tool. Interrater reliability ratings for the 2 coding teams were κ = 0.94 and κ = 0.89. Patients primarily conceptualized AI in terms of cognition. Increased diagnostic speed (29 participants [60%]) and health care access (29 [60%]) were the most commonly perceived benefits of AI for skin cancer screening; increased patient anxiety was the most commonly perceived risk (19 [40%]). Patients perceived both more accurate diagnosis (33 [69%]) and less accurate diagnosis (41 [85%]) to be the greatest strength and weakness of AI, respectively. The dominant theme that emerged was the importance of symbiosis between humans and AI (45 [94%]). Seeking biopsy was the most common response to conflict between human and AI clinical decision-making (32 [67%]). Overall, 36 patients (75%) would recommend AI to family members and friends. In this qualitative study, patients appeared to be receptive to the use of AI for skin cancer screening if implemented in a manner that preserves the integrity of the human physician-patient relationship. This qualitative study examines how the use of artificial intelligence as a screening tool is perceived by patients receiving care in a dermatology clinic.
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                Author and article information

                Contributors
                Journal
                JMIRx Med
                JMIRx Med
                JMIRxMed
                xmed
                34
                JMIRx Med
                JMIRx Med
                2563-6316
                2024
                25 March 2024
                : 5
                : e50803
                Affiliations
                [1 ]departmentBig Data Department , Hospital Israelita Albert Einstein , Sao Paulo, Brazil
                Author notes
                MaraGiavina-BianchiMD, PhD, Big Data Department, Hospital Israelita Albert Einstein, Av Albert Einstein 627, Morumbi, Sao Paulo, 05652-900, Brazil, 55 1121511233; marahgbianchi@ 123456gmail.com

                None declared.

                Author information
                http://orcid.org/0000-0001-7059-4068
                http://orcid.org/0000-0002-5889-1382
                http://orcid.org/0000-0001-7119-4170
                Article
                50803
                10.2196/50803
                11080601
                38535503
                53a5e71c-0447-4057-8d74-01d4ce0ebe3a
                Copyright © Mara Giavina-Bianchi, Edson Amaro Jr, Birajara Soares Machado. Originally published in JMIRx Med (https://med.jmirx.org)

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIRx Med, is properly cited. The complete bibliographic information, a link to the original publication on https://med.jmirx.org/, as well as this copyright and license information must be included.

                History
                : 12 July 2023
                : 28 December 2023
                : 13 January 2024
                Categories
                Original Paper
                #xHealthInformatics
                Artificial Intelligence
                Artificial Intelligence
                #xHealthSystemsandQualityImprovement
                #xMedicalEducation
                #xHealthSystemsandQualityImprovement
                #xMedicalEducation

                artificial intelligence,adoption,ai,acceptance,opinion, perceptions,survey,expectations,physician,medical survey,qualitative study

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