Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 28,711 to 28,720 of 219,260 articles

Perceptions of Health Care Professionals on the Integration and Use of AI in Clinical Cancer Care: Interview Study.

JMIR human factors Apr 20, 2026
BACKGROUND: Artificial intelligence (AI) is increasingly recognized for its potential to transform cancer care. However, much of the existing evidence of its efficacy comes from controlled settings. There remains a need to complement this knowledge w... read more 

Use of Digital Technology for Developing Communication Skills in Undergraduate and Postgraduate Medical Education: Scoping Review.

JMIR medical education Apr 20, 2026
BACKGROUND: Effective doctor-patient communication is fundamental to safe, high-quality health care and is a core competency across undergraduate and postgraduate medical education. Communication skills training (CST) has traditionally relied on work... read more 

Path planning for volumetric flask grasping based on visual guidance and multi-constraint optimization.

PloS one Apr 20, 2026
In the automated operation of the chemical laboratory, using a robotic arm to pick up volumetric flasks is a core step of the operation process. By implementing reasonable path planning, the grasping operation of the robotic arm can be made efficient... read more 

CDR-Net: A computerized framework to detect Alzheimer's diseases and mild cognitive impairment.

PloS one Apr 20, 2026
Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two dementia-related brain illnesses that are prevalent among elders in the twenty-first century. MCI is treated as the initial stage of AD, and once the illness reaches the AD stage, t... read more 

Popular Online Content as a Treatment-as-Usual Control in Digital Mental Health Intervention Trials: Secondary Analysis of Two Online Randomized Controlled Trials With Repeated Measures.

JMIR mental health Apr 20, 2026
BACKGROUND: Treatment-as-usual (TAU) conditions are intended to reflect the support typically received in routine treatment settings. For digital mental health interventions (DMHIs) delivered online, TAU conditions should reflect the usual patterns o... read more 

Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC.

PLOS digital health Apr 20, 2026
Current image-based deep learning models that predict the benefits of immunotherapy in non-small cell lung cancer (NSCLC) require high-performance hardware. We aimed to develop and externally validate a clinician-operable prognostic model that integr... read more 

Enhancing flood prediction through physics-driven typhoon feature engineering and machine learning.

PloS one Apr 20, 2026
Typhoon-induced extreme floods pose severe threats to subtropical watersheds, yet systematic integration of typhoon physics into machine learning flood prediction remains limited. This study developed a physics-informed machine learning framework for... read more 

Telemedicine adoption in cardiology: Determinants and predictors identified using Bayesian Model Averaging and Machine Learning.

PLOS digital health Apr 20, 2026
In this secondary analysis of a German cross-sectional survey data, we investigated key determinants and predictors of telemedicine (TM) use among healthcare professionals (HCPs) treating cardiology patients. We applied Bayesian Model Averaging (BMA)... read more 

Power transformer online monitoring and state assessment method based on 2DCNN-BiLSTM multi-source feature fusion.

PloS one Apr 20, 2026
As key power system equipment, real-time monitoring and accurate assessment of power transformers' operating status are critical for grid safety. To address deficiencies in existing methods regarding multi-source heterogeneous data fusion, unstructur... read more 

A cross-dataset harmonized intrusion detection framework with statistically validated multi-model learning.

PloS one Apr 20, 2026
Intrusion Detection Systems (IDS) are considered critical security tools in ensuring network infrastructure security. However, recent studies on machine learning-based IDS systems are often constrained by their heavy dependence on a single dataset, l... read more 
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