Objectives: To survey UK doctors on their uses of artificial intelligence (AI) and of their views on the ethics and regulation of AI in healthcare.
Design: Anonymous cross-sectional e-survey.
Setting: An online survey of UK General Medical Council (GMC) registered doctors.
Participants: 272 individuals.
Main Outcome Measures: Likert-scale responses to questions covering personal use of AI, concerns about AI, requirements for introduction of AI and views on necessary AI regulation in healthcare.
Results: Most doctors rated themselves as slightly or moderately knowledgeable about AI, with men rating their knowledge levels higher than women. Doctors in training are more likely to have used AI than doctors after training. 37% of doctors who use AI reported using AI to help write the required reflective pieces for their portfolio. Doctors reported concerns about AI regarding patient safety and patients' right to confidentiality. They also expressed a strong desire for further regulation of AI in healthcare and, specifically, for their professional bodies to draft guidelines for the use of AI by doctors.
Conclusions: This study provides useful insights into UK doctors' uses of AI in healthcare and their opinions on its introduction and regulation. It provides a case for guidance on the use of AI in the reflective practices of doctors and for further evaluation of doctors' concerns about AI in healthcare. We call on doctors' professional bodies (GMC, BMA and royal colleges) to draft professional guidance for doctors using AI.
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http://dx.doi.org/10.1136/bmjopen-2024-089090 | DOI Listing |
J Med Internet Res
January 2025
Graduate School of Health Science and Technology, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
Background: Artificial intelligence (AI) social chatbots represent a major advancement in merging technology with mental health, offering benefits through natural and emotional communication. Unlike task-oriented chatbots, social chatbots build relationships and provide social support, which can positively impact mental health outcomes like loneliness and social anxiety. However, the specific effects and mechanisms through which these chatbots influence mental health remain underexplored.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Black Dog Institute, University of New South Wales, Sydney, Australia.
Background: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment methods are more effective than traditional methods such as newspapers, media, or flyers is inconsistent. Here we present insights from our experience recruiting tertiary education students for a digital mental health artificial intelligence-driven adaptive trial-Vibe Up.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
School of Management, Hefei University of Technology, Hefei, China.
Background: In online mental health communities, the interactions among members can significantly reduce their psychological distress and enhance their mental well-being. The overall quality of support from others varies due to differences in people's capacities to help others. This results in some support seekers' needs being met, while others remain unresolved.
View Article and Find Full Text PDFJ Vector Borne Dis
October 2024
Department of Biological Sciences, King AbdulAziz University, Jeddah, Makkah, Saudi Arabia.
Background Objectives: In malaria infection, quantifying blood parasitemia is a critical step for evaluating the severity of the disease. This has generally been conducted manually, and thus, its accuracy depends on the expertise of technicians. There is an urgent need for an automated technique to overcome manual errors.
View Article and Find Full Text PDFPLoS One
January 2025
College of Arts, Anhui Xinhua University, Hefei, China.
To improve the expressiveness and realism of illustration images, the experiment innovatively combines the attention mechanism with the cycle consistency adversarial network and proposes an efficient style transfer method for illustration images. The model comprehensively utilizes the image restoration and style transfer capabilities of the attention mechanism and the cycle consistency adversarial network, and introduces an improved attention module, which can adaptively highlight the key visual elements in the illustration, thereby maintaining artistic integrity during the style transfer process. Through a series of quantitative and qualitative experiments, high-quality style transfer is achieved, especially while retaining the original features of the illustration.
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