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.
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http://dx.doi.org/10.1055/s-0039-1677891 | 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.
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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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