AIMC Topic: Pilot Projects
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OB HUB: Remote Electronic Fetal Monitoring Surveillance.
MCN. The American journal of maternal child nursing
May 13, 2025
OBJECTIVE: The purpose of this project was to implement a remote fetal surveillance unit with increased vigilance and timelier responses to electronic fetal monitor tracings to improve neonatal outcomes and increase safety.
Impact of a "Digital Health" Curriculum on Students' Perception About Competence and Relevance of Digital Health Topics for Future Professional Challenges: Prospective Pilot Study.
JMIR formative research
May 12, 2025
BACKGROUND: The rapid integration of digital technologies in health care has emphasized the need to ensure that medical students are well-equipped with the knowledge and competencies related to digital health.
Ethics From the Outset: Incorporating Ethical Considerations into the Artificial Intelligence and Technology Collaboratories for Aging Research Pilot Projects.
The journals of gerontology. Series A, Biological sciences and medical sciences
May 5, 2025
There is an urgent need to develop tools to enable older adults to live healthy, independent lives for as long as possible. To address this need, the National Institute on Aging (NIA) Artificial Intelligence and Technology Collaboratories (AITCs) for...
Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians.
JAMA network open
May 1, 2025
IMPORTANCE: The increase of electronic health record (EHR) work negatively impacts clinician well-being. One potential solution is incorporating an ambient artificial intelligence (AI) documentation platform.
Treatment of Porphyria Cutanea Tarda Scarring With Combination Laser Treatment and a Pilot Use of Artificial Intelligence to Quantify Laser Results.
Journal of cosmetic dermatology
Apr 1, 2025
BACKGROUND: Porphyria cutanea tarda (PCT) is the most common subtype of porphyria and results from a deficiency of the enzyme uroporphyrinogen decarboxylase. Even after successful treatment, patients can be left with significant scarring, and there i...
Using machine learning models to identify severe subjective cognitive decline and related factors in nurses during the menopause transition: a pilot study.
Menopause (New York, N.Y.)
Apr 1, 2025
OBJECTIVE: This study aims to develop and validate a machine learning model for identifying individuals within the nursing population experiencing severe subjective cognitive decline (SCD) during the menopause transition, along with their associated ...
Deep learning implementation for extrahepatic bile duct detection during indocyanine green fluorescence-guided laparoscopic cholecystectomy: pilot study.
BJS open
Mar 4, 2025
BACKGROUND: A real-time deep learning system was developed to identify the extrahepatic bile ducts during indocyanine green fluorescence-guided laparoscopic cholecystectomy.
Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading.
The journal of pathology. Clinical research
Mar 1, 2025
This work aimed to evaluate both the usefulness and user acceptance of five gradient-based explainable artificial intelligence (XAI) methods in the use case of a prostate carcinoma clinical decision support system environment. In addition, we aimed t...
A Pilot Study on Using an Artificial Intelligence Algorithm to Identify Urolith Composition through Abdominal Radiographs in the Dog.
Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Mar 1, 2025
In small animal practice, patients often present with urinary lithiasis, and prediction of urolith composition is essential to determine the appropriate treatment. Through abdominal radiographs, the composition of mineral radiopaque uroliths can be d...
Developing and validating a prediction tool for cerebral amyloid angiopathy neuropathological severity.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Mar 1, 2025
INTRODUCTION: Cerebral amyloid angiopathy (CAA) is a cerebrovascular condition, the severity of which can only be determined post mortem. Here, we developed machine learning models, the Florey CAA Score (FCAAS), to predict CAA severity (none/mild/mod...
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