AIMC Topic: Artificial Intelligence

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Shall we call for a doctor? How to build trust toward AI in healthcare: Insights from a Polish cross-sectional preference study.

Health policy (Amsterdam, Netherlands)
OBJECTIVES: This research aimed to investigate key success factors for the adoption of AI-driven health technologies, particularly in healthcare ecosystems of low digital literacy, such as Poland.

Application Value of Deep Learning-Based AI Model in the Classification of Breast Nodules.

British journal of hospital medicine (London, England : 2005)
Breast nodules are highly prevalent among women, and ultrasound is a widely used screening tool. However, single ultrasound examinations often result in high false-positive rates, leading to unnecessary biopsies. Artificial intelligence (AI) has dem...

Areas of research focus and trends in the research on the application of AIGC in healthcare.

Journal of health, population, and nutrition
BACKGROUND: As a crucial part of current AI technology, the extent of AIGC's (Artificial Intelligence Generated Content) impact on healthcare, its potential to further drive the development of intelligent healthcare, and its ability to alleviate the ...

Artificial Intelligence in cancer epigenomics: a review on advances in pan-cancer detection and precision medicine.

Epigenetics & chromatin
DNA methylation is a fundamental epigenetic modification that regulates gene expression and maintains genomic stability. Consequently, DNA methylation remains a key biomarker in cancer research, playing a vital role in diagnosis, prognosis, and tailo...

AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.

Clinical and experimental medicine
The SARS-CoV-2 RNA virus, with its rapid spread and frequent genetic changes, has posed unparalleled obstacles for public health and treatment efforts. Early diagnosis of the disease and the development of effective treatment strategies are the main ...

Dataset resulting from the user study on comprehensibility of explainable AI algorithms.

Scientific data
This paper introduces a dataset that is the result of a user study on the comprehensibility of explainable artificial intelligence (XAI) algorithms. The study participants were recruited from 149 candidates to form three groups representing experts i...

Clinical feasibility of AI Doctors: Evaluating the replacement potential of large language models in outpatient settings for central nervous system tumors.

International journal of medical informatics
BACKGROUND AND OBJECTIVES: The treatment of central nervous system (CNS) tumors is complex and resource-intensive, with higher mortality in underserved regions. Large language models (LLMs) show promise in medical support, but their real-world perfor...

Toward a general framework for AI-enabled prediction in crop improvement.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
A theoretical framework for AI and ensembled prediction for crop improvement is introduced and demonstrated using the logistic map. Symbolic/sub-symbolic AI-based prediction can increase predictive skill with increase in system complexity. The curse ...

Digital Therapeutics for Cognitive Impairment: Exploring Innovations, Challenges, and Future Prospects.

Journal of medical Internet research
Recent advancements in cognitive neuroscience and digital technology have significantly accelerated the adoption of digital therapeutics for cognitive impairment. This viewpoint explores the innovative applications of digital therapeutics in the asse...