AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial.

July 24, 2026| Source: Nature medicine

The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rely on resource-intensive phenotyping, multidisciplinary expertise and genetic testing. Here we developed Retina4IRD, an artificial intelligence (AI)-based clinician decision support system (CDSS) that predicts 17 genotype categories from retina images. Retina4IRD uses a Vision Transformer model pretrained with RETFound. We then trained and validated Retina4IRD using multimodal data with color fundus photographs and optical coherence tomography scans from 1,843 genetically confirmed patients (3,376 eyes) across China, South Korea and Poland. The top-5 prediction accuracy was 0.904 (95% confidence interval (CI): 0.896-0.912) and 0.856 (95% CI: 0.850-0.863) for internal and external validation, respectively. We conducted a randomized controlled trial with 300 participants with suspected IRD randomized 1:1 to either Retina4IRD-assisted specialist arm or specialist-only arm. Of these, 295 participants (median age 33 years, 114 (38.6%) females) with available next-generation sequencing reports were included in the final analysis. The primary outcome was met: top-5 genetic accuracy was significantly higher in the Retina4IRD-assisted specialist arm versus the specialist-only arm (88.5% versus 67.3%, P < 0.001). For secondary endpoints, top-1 to top-4 accuracies all favored the Retina4IRD-assisted specialist arm, with top-1 accuracy of 37.8% versus 22.4% and top-4 accuracy of 81.8% versus 53.1%, respectively. Post hoc analyses demonstrated that, with Retina4IRD assistance, clinicians made better management decisions, and the composite downstream management score indicated significantly higher scores relative to the control group (37.7 versus 28.5, P < 0.001). Our study shows that Retina4IRD is a CDSS tool prior to genetic testing and aligns with clinical workflow for patients with suspected IRDs. ClinicalTrials.gov identifier: NCT06839170 .

Read the original source →

PLK1-mediated phosphorylation of PHGDH reprograms serine metabolism in advanced prostate cancer.

Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to meet their increased biosynthetic and energetic demands. Although cells possess the capacity for de novo serine biosynthesis, most transformed cancer cells preferentially rely on exogenous serine uptake to sustain their growth, yet the regulatory mechanisms driving this metabolic dependency remain poorly understood. Here, we uncover a mechanism by which Polo-like kinase 1 (PLK1), frequently overexpressed in prostate cancer,

Artificial IntelligenceAugust 4, 2026

When coordination is avoidable: A monotonicity analysis of organizational tasks.

Organizations devote substantial resources to coordination, yet which tasks actually require it for correctness remains unclear. The problem is acute in multiagent AI systems, where coordination cost is directly measurable and can exceed the cost of the work itself. Distributed systems theory provides a precise criterion: Coordination is required when a task specification is nonmonotonic, meaning that as histories grow, new information can invalidate prior conclusions. Here we show that Thompson

Artificial IntelligenceJuly 30, 2026

Dapagliflozin and Acute Kidney Injury Following Cardiac Surgery: A Randomized Clinical Trial.

Two percent to 50% of patients undergoing elective cardiac surgery experience acute kidney injury (AKI) postoperatively. Medications to prevent AKI after elective cardiac surgery have not been identified. In patients undergoing elective cardiac surgery, to evaluate whether initiating dapagliflozin 1 day prior to surgery reduces the incidence of AKI at 7 days after cardiac surgery, compared with placebo. Multicenter, double-blind, placebo-controlled randomized clinical trial conducted at 2 academ

Artificial IntelligenceJuly 28, 2026

Context-aware multimodal AI navigates hidden pathways in five centuries of art evolution.

The rise of multimodal generative AI transforms the intersection of technology and art, offering richer insights into large-scale artworks. While significant research has focused on their creative potential, their ability to represent artworks in latent spaces remains underexamined. We use generative AI, specifically Stable Diffusion, to analyze 500 y of Western paintings by extracting two types of latent information with the model: formal aspects (e.g., colors) and contextual aspects (e.g., sub

Artificial IntelligenceJuly 27, 2026

Advancing cancer detection and treatment using longitudinal routine clinical data.

Cancer management remains fragmented across its continuum, from late-stage diagnosis and salvage therapies to non-personalized surveillance. Here, we present Oncoformer, a unified multimodal transformer model trained on the China Oncology Multimodal Prediction and Surveillance Study (COMPASS) cohort (3.67 million individuals, 17.7 million clinical visits) and validated on independent external cohorts, including the UK Biobank. Oncoformer integrates longitudinal electronic health records with che