On the relationships between apathy, depression and anhedonia.
Exploring Apathy, Depression, and Anhedonia: Key Findings from a Comprehensive Study π§ π
March 28, 2026
Follow us
latest articles
Discover the newest research about AI innovations in π€ Machine Learning.
Emerging perspectives in cardiovascular medicine.
Emerging trends in cardiovascular care: AI diagnostics, risk stratification, and rare cardiomyopathies. πβ€οΈ
March 28, 2026
Antimicrobial discovery from underexplored environments: unlocking specialized metabolism.
Exploring new antibiotic sources: AI, multi-omics, and ecology combat AMR crisis. π¦ π¬π
March 27, 2026
SGA-DT: An adaptive fusion framework for missing data imputation and interpretable healthcare classification.
SGA-DT framework enhances healthcare predictions by addressing missing data with adaptive strategies. ππ€ Robust, interpretable, and accurate!
March 27, 2026
Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.
Radiomics & AI enhance tumor response prediction in GI neoplasms. Key findings: ORs range from 8.12 to 11.62. ππ
March 26, 2026
Comment on Topaloglu et al. Machine Learning-Driven Lung Sound Analysis: Novel Methodology for Asthma Diagnosis. Adv. Respir. Med. 2025, 93, 32.
Exploring machine learning in asthma diagnosis: Topaloglu et al.’s innovative lung sound analysis method. ππ«
March 26, 2026
Validity and Reliability of an AI-Based Jump Height App vs. Infrared Contact Mat: Minimal Influence of Skin Pigmentation Under Standardized Lighting.
AI Jump Height App vs. Infrared Mat: Validity (RΒ²=0.94) & Reliability (ICC=0.97) Confirmed! ππ€
March 25, 2026
Machine learning approached a 14-item shortened version of the Positive And Negative Sleep Appraisal Measure (PANSAM-14).
Machine learning refines sleep appraisal: PANSAM-14 shows RΒ² scores up to 0.95 for predictive accuracy. π€π
March 25, 2026
Extubation Decision Support in Critical Care: A Multimodal Machine Learning Framework Integrating Segmented Radiographs and Routine Clinical Data.
Multimodal ML enhances extubation decisions in critical care, achieving 79.46% accuracy. Key metrics: respiratory rates, tidal volumes, demographics. ππ©Ί
March 24, 2026
Artificial Intelligence Applications in COVID-19-Associated Coagulopathy: Lessons Learned.
AI in COVID-19 Coagulopathy: D-dimer’s Role, Thrombotic Risks, and Future Directions ππ©Έ
March 24, 2026