Physical Address
304 North Cardinal St.
Dorchester Center, MA 02124
A multicenter validation of ELDER-ICU, a machine learning model for predicting mortality in elderly ICU patients, reveals critical strategies for successfully deploying clinical AI across diverse international populations. Background Machine learning models developed on single populations often underperform when deployed…
A new study demonstrates that machine learning-based prognostic models can meaningfully improve accuracy and decision-making efficiency for surgeons managing colorectal liver metastases, particularly benefiting less experienced clinicians. Background Colorectal liver metastases (CRLM) require accurate prognostic assessment to guide treatment decisions.…
Researchers developed VesselNet, a deep learning model that predicts hemoglobin and red blood cell counts from brief magnified videos of eye blood vessels, offering noninvasive point-of-care blood testing. Background Traditional blood testing requires venipuncture. This study of 224 participants evaluated…
Large language models are now competitive—and often superior—to traditional machine learning for clinical prediction tasks, according to a comprehensive benchmark study. Background The clinical AI field has traditionally emphasized specialized models, assuming LLMs are ill-suited for prediction. ClinicRealm challenges this…
Researchers developed AI models that estimate left ventricular ejection fraction from electrocardiograms, offering an accessible alternative to echocardiography. Background Left ventricular ejection fraction (LVEF) is typically assessed by echocardiography. Thambiraj and colleagues developed machine learning models to estimate LVEF directly…
Novel machine learning models predict 1-year stroke risk in newly diagnosed atrial fibrillation with dramatic improvements over the standard CHA₂DS₂-VASc score, offering clinicians better tools for personalized anticoagulation decisions. Background Stroke prevention in atrial fibrillation relies on the CHA₂DS₂-VASc risk…
ChatGPT-5 Pro can generate clinically realistic psychiatric teaching cases, but safety concerns require expert review before classroom use. Background AI tools increasingly support medical education. This study evaluated whether ChatGPT-5 Pro could generate realistic psychiatric diagnostic vignettes depicting patient chatbot…
A new lightweight artificial intelligence model combined with salivary metabolic analysis achieved 91.9% accuracy in identifying patients with both type 2 diabetes and periodontitis in just 0.7 minutes per test, offering a rapid non-invasive screening tool. Background Periodontitis frequently co-occurs…
Decipher-MR represents a significant advance in medical imaging AI, offering a reusable foundation model trained on 200,000 diverse MRI scans that consistently outperforms existing approaches across disease detection, anatomy localization, and cross-modal retrieval tasks. Background Medical imaging AI has historically…
A new study shows that radiology AI models fine-tuned on institutional data outperform general-purpose language models like GPT-4.1 in clinical acceptance, challenging assumptions about off-the-shelf AI solutions in healthcare. Background As artificial intelligence increasingly enters clinical practice, radiologists face a…
