University of Edinburgh

On structural MRI from three independent cohorts, Alzheimer's disease classifiers use age as a shortcut: older patients receive higher predicted risk. Residualizing age linearly in feature space (PMDN) brings the age-prediction correlation near zero in cognitively normal and mild cognitive impairment groups, with balanced accuracy 0.71 and Brier score about 0.22, and far less fold-to-fold spread than the adversarial CF-Net. Post hoc calibration further lowers probability error, with nonparametric isotonic regression and Bayesian binning performing best.

On structural MRI from three independent cohorts, Alzheimer's disease classifiers use age as a shortcut: older patients receive higher predicted risk. Residualizing age linearly in feature space (PMDN) brings the age-prediction correlation near zero in cognitively normal and mild cognitive impairment groups, with balanced accuracy 0.71 and Brier score about 0.22, and far less fold-to-fold spread than the adversarial CF-Net. Post hoc calibration further lowers probability error, with nonparametric isotonic regression and Bayesian binning performing best.

Molecular property prediction has mostly gone from structural self-supervision straight to endpoint fine-tuning, skipping the sparse experimental activity data in between. GRASP is a 93.5M-parameter graph Transformer trained in two stages: replaced-token detection on 1.54 billion ZINC20 molecule presentations, then adaptation on 642 ChEMBL assays. On 23 cluster-held-out OpenADMET endpoints, full fine-tuning reaches a mean MAE of 0.374, versus 0.383 for CheMeleon, 0.408 for ECFP4-LightGBM and 0.440 for Chemprop; adding ChEMBL adaptation lowers mean MAE from 0.407 to 0.374 and improves 21 of 23 endpoints.

Molecular property prediction has mostly gone from structural self-supervision straight to endpoint fine-tuning, skipping the sparse experimental activity data in between. GRASP is a 93.5M-parameter graph Transformer trained in two stages: replaced-token detection on 1.54 billion ZINC20 molecule presentations, then adaptation on 642 ChEMBL assays. On 23 cluster-held-out OpenADMET endpoints, full fine-tuning reaches a mean MAE of 0.374, versus 0.383 for CheMeleon, 0.408 for ECFP4-LightGBM and 0.440 for Chemprop; adding ChEMBL adaptation lowers mean MAE from 0.407 to 0.374 and improves 21 of 23 endpoints.

This review synthesizes evidence on opportunistically identifying and quantifying coronary artery calcium (CAC) on thoracic CT performed for other clinical indications (routine thoracic CT, lung cancer screening CT, attenuation correction CT), reporting a pooled CAC prevalence of 52%, independent associations with all-cause mortality (pooled relative risk 2.13) and major adverse cardiovascular events (pooled relative risk 2.61), and reviewing novel applications including AI/machine learning automated scoring, radiomics, photon-counting CT, and CAC as a multi-system biomarker, while noting that no randomized trials assessing the clinical impact of this approach have yet been published.

This review synthesizes evidence on opportunistically identifying and quantifying coronary artery calcium (CAC) on thoracic CT performed for other clinical indications (routine thoracic CT, lung cancer screening CT, attenuation correction CT), reporting a pooled CAC prevalence of 52%, independent associations with all-cause mortality (pooled relative risk 2.13) and major adverse cardiovascular events (pooled relative risk 2.61), and reviewing novel applications including AI/machine learning automated scoring, radiomics, photon-counting CT, and CAC as a multi-system biomarker, while noting that no randomized trials assessing the clinical impact of this approach have yet been published.

This discussion article systematically maps six ways digital healthcare could address unmet needs in functional neurological disorder (FND)—using large biobank and clinic datasets to reveal epidemiology, comorbidity, mechanisms, economic burden and possibly treatment effects; augmenting complex and time-consuming assessment processes that exceed clinical capacity with artificial intelligence; improving diagnostic precision through automated tremor analysis, quantification of functional motor signs and speech recognition; improving self-management and access to therapy via online tools and telehealth; improving outcome measurement and existing therapy with wearables and telehealth; and developing new therapies such as AI-assisted therapies, biofeedback and virtual or augmented reality—while

This discussion article systematically maps six ways digital healthcare could address unmet needs in functional neurological disorder (FND)—using large biobank and clinic datasets to reveal epidemiology, comorbidity, mechanisms, economic burden and possibly treatment effects; augmenting complex and time-consuming assessment processes that exceed clinical capacity with artificial intelligence; improving diagnostic precision through automated tremor analysis, quantification of functional motor signs and speech recognition; improving self-management and access to therapy via online tools and telehealth; improving outcome measurement and existing therapy with wearables and telehealth; and developing new therapies such as AI-assisted therapies, biofeedback and virtual or augmented reality—while