Current medical AI articles

Original Title: A randomized controlled trial of a WeChat-based artificial intelligence agent for postoperative care in orthopedic patients Journal: NPJ digital medicine DOI: 10.1038/s41746-025-02269-8 Overview This randomized controlled trial evaluated a GPT-4-powered artificial intelligence agent delivered via the WeChat platform to support postoperative recovery in orthopedic patients. The study included 261 participants, with 140 assigned…

Original Title: Multimodal intelligent prediction model for in vitro fertilization Journal: NPJ digital medicine DOI: 10.1038/s41746-025-02331-5 Overview This study introduces VaTEP, a multimodal deep learning framework that integrates time-lapse system videos of developing embryos with tabular clinical data. Developed and validated using data from 9,786 participants across three medical centers, VaTEP predicts three clinical outcomes:…

Original Title: KT-LLM: an evidence-grounded and sequence text framework for auditable kidney transplant modeling Journal: NPJ digital medicine DOI: 10.1038/s41746-025-02323-5 Overview The management of kidney transplantation involves complex longitudinal data and strict regulatory policies that are often difficult to align. This study presents KT-LLM, a framework designed to bridge the gap between structured patient follow-up…

Original Title: Biomarkers Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70856_107381 Overview Dementia represents a significant health challenge characterized by cognitive decline that interferes with daily living. Clinical diagnosis often occurs in advanced stages, necessitating early detection methods to improve patient outcomes. This study investigates deep learning algorithms to classify…

Original Title: Biomarkers Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70856_106312 Overview Accurate differential diagnosis of dementia types is essential for appropriate treatment. This study utilizes T1-weighted magnetic resonance imaging data and a deep learning approach to distinguish between Alzheimer’s disease and other forms of cognitive impairment. The researchers focus…

Original Title: Biomarkers Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70856_107467 Overview The study investigates the utility of spontaneous speech as a non-invasive biomarker for Alzheimer's disease by developing an automated analysis pipeline. Utilizing the ADReSS 2020 Challenge dataset, which comprises audio recordings from 108 training participants and 48 testing…

Original Title: Biomarkers Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70856_107471 Overview Alzheimer's disease involves grey matter loss in regions like the hippocampus. Accurate atrophy measurement is essential for monitoring progression. Deformation Based Morphometry (DBM) quantifies these changes but is limited by the 1 millimeter cubed resolution of standard Magnetic…

Original Title: Interpretable deep learning for multicenter gastric cancer T staging from CT images Journal: NPJ digital medicine DOI: 10.1038/s41746-025-02002-5 Overview Gastric cancer remains a significant global health challenge, requiring precise preoperative T staging to determine the appropriate therapeutic strategy, such as neoadjuvant chemotherapy or direct surgical intervention. Standard contrast-enhanced computed tomography is the primary…

Original Title: Dementia Care Research and Psychosocial Factors Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70858_098721 Overview Cognitive impairment is a significant global health challenge, affecting approximately 32% of adults over the age of 65. While cognitive rehabilitation is an established method to maintain independence and delay the need for…

Original Title: Basic Science and Pathogenesis Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70855_107083 Overview This research addresses the challenge of predicting the progression of Alzheimer’s disease and related dementias using survival analysis. While deep learning models offer high predictive performance, their complex architectures often obscure the biological factors driving…