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Integrating genomics, transcriptomics, proteomics and metabolomics with machine learning enables more precise and tailored therapeutic strategies that improve treatment efficacy and reduce adverse effects.

AI adoption in organizations can improve work practices by increasing efficiency, productivity, and innovation.

AI models predicted incident hypertension and cardiovascular risk from EHRs, wearables and multimodal data with AUCs around 0.75 to 0.90, supporting personalized therapy and remote monitoring.

Deep learning models, especially U-Net variants and newer transformer ensembles, improved ischemic stroke lesion segmentation on MRI, achieving Dice scores above 0.80 and approaching 0.90 in multisite DWI reports, to support faster treatment selection and quantification.

LLM chatbots answered periodontal patient questions with scientific accuracy comparable to expert periodontologists while scoring higher on completeness and empathy.

Explainable AI methods such as SHAP and Grad-CAM improve food quality control by identifying which spectral wavelengths or image regions drive predictions, increasing transparency for inspectors verifying contaminant detection and freshness assessments.

UBNet-Seg, a 2.3M-parameter U-Net variant using lung fields as geometric proxy, achieved 95.85% lung Dice at 0.05s inference and 90.31% automated cardiomegaly accuracy on NIH, rising to 93.63% on NIH and 91.21% on OpenI after expert-guided refinement.

ML and DL analysis of imaging, genetics, and behavioral data can improve early identification and diagnostic accuracy for conditions like depression, bipolar disorder, and schizophrenia.

Synthetic relationships with AI companions could reduce loneliness by providing always-available, adaptive, emotionally responsive interaction that fosters companionship and lowers social anxiety.

VSCS framework enables community members to act as co-researchers and translate local values into technical requirements for AI systems.

Chinese and South Korean regulatory toolkits for AI journalism on platforms like Toutiao and Naver were found to mitigate risks of digital infodemics and algorithmic bias.

Data-driven machine learning models can rapidly generate biomolecular structures and propose conformational ensembles for recognition events with high predictive performance.

MLFFs provide high accuracy with high efficiency for atomic-level studies of inorganic crystalline materials, overcoming traditional limits in structure prediction, properties, defects, and phase transitions.

First-year medical students showed a modest net improvement in accuracy after reviewing ChatGPT-generated answers, because incorrect-to-correct changes exceeded correct-to-incorrect changes.

Australian creatives would retain ownership and control of their work and receive payment through licensing deals with AI companies, rather than having their books, music, art and news used for free to train models.