AIMC Topic: Artificial Intelligence
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Ethics in Patient Preferences for Artificial Intelligence-Drafted Responses to Electronic Messages.
JAMA network open
Mar 3, 2025
IMPORTANCE: The rise of patient messages sent to clinicians via a patient portal has directly led to physician burnout and dissatisfaction, prompting uptake of artificial intelligence (AI) to alleviate this burden. It is important to understand patie...
Detecting Artificial Intelligence-Generated Versus Human-Written Medical Student Essays: Semirandomized Controlled Study.
JMIR medical education
Mar 3, 2025
BACKGROUND: Large language models, exemplified by ChatGPT, have reached a level of sophistication that makes distinguishing between human- and artificial intelligence (AI)-generated texts increasingly challenging. This has raised concerns in academia...
Reliability and validity of a novel single-lead portable electrocardiogram device for pregnant women: a comparative study.
BMC medical informatics and decision making
Mar 3, 2025
BACKGROUND: WenXinWuYang, a novel portable Artificial Intelligence Electrocardiogram (AI-ECG) device, can detect many kinds of abnormal heart disease and perform a single-lead ECG, but its reliability and validity among pregnant women is unclear. The...
Exploring a digital music teaching model integrated with recurrent neural networks under artificial intelligence.
Scientific reports
Mar 3, 2025
This study proposes an intelligent digital music teaching model based on Artificial Intelligence (AI) and Long Short-Term Memory (LSTM) networks to enhance personalized assessment and feedback in music education. Within this teaching model, the music...
Performance of a point-of-care ultrasound platform for artificial intelligence-enabled assessment of pulmonary B-lines.
Cardiovascular ultrasound
Mar 3, 2025
BACKGROUND: The incorporation of artificial intelligence (AI) into point-of-care ultrasound (POCUS) platforms has rapidly increased. The number of B-lines present on lung ultrasound (LUS) serve as a useful tool for the assessment of pulmonary congest...
Recommendations for Artificial Intelligence Application in Continued Process Verification: A Journey Toward the Challenges and Benefits of AI in the Biopharmaceutical Industry.
PDA journal of pharmaceutical science and technology
Mar 3, 2025
This review paper explores the transformative impact of Artificial Intelligence (AI) on Continued Process Verification (CPV) in the biopharmaceutical industry. Originating from the CPV of the Future project, the study investigates the challenges and ...
The Extent to Which Artificial Intelligence Can Help Fulfill Metastatic Breast Cancer Patient Healthcare Needs: A Mixed-Methods Study.
Current oncology (Toronto, Ont.)
Mar 2, 2025
The Artificial Intelligence Patient Librarian (AIPL) was designed to meet the psychosocial and supportive care needs of Metastatic Breast Cancer (MBC) patients with HR+/HER2- subtypes. AIPL provides conversational patient education, answers user ques...
Prediction of Postoperative Macular Hole Status by Automated Preoperative Retinal OCT Analysis: A Narrative Review.
Ophthalmic surgery, lasers & imaging retina
Mar 1, 2025
Optical coherence tomography (OCT) is a non-invasive imaging modality essential for macular hole (MH) management. Artificial intelligence (AI) algorithms could be applied to OCT to garner insights for MH prognosis and outcomes. The objective was to r...
Essential competencies of nurses working with AI-driven lifestyle monitoring in long-term care: A modified Delphi study.
Nurse education today
Mar 1, 2025
BACKGROUND: As more and more older adults prefer to stay in their homes as they age, there's a need for technology to support this. A relevant technology is Artificial Intelligence (AI)-driven lifestyle monitoring, utilizing data from sensors placed ...
Artificial intelligence based predictive tools for identifying type 2 diabetes patients at high risk of treatment Non-adherence: A systematic review.
International journal of medical informatics
Mar 1, 2025
AIMS: Several Artificial Intelligence (AI) based predictive tools have been developed to predict non-adherence among patients with type 2 diabetes (T2D). Hence, this study aimed to describe and evaluate the methodological quality of AI based predicti...
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