All Work Published on Healthcare
Promoting Algorithmic Fairness in Clinical Risk Prediction
-1.png&w=256&q=80)
This brief examines the debate on algorithmic fairness in clinical predictive algorithms and recommends paths to safer, more equitable healthcare AI.
This brief examines the debate on algorithmic fairness in clinical predictive algorithms and recommends paths to safer, more equitable healthcare AI.
-1.png&w=256&q=100)
Why 'Zero-Shot' Clinical Predictions Are Risky
These models generate plausible timelines from historical patterns; without calibration and auditing, their “probabilities” may not reflect reality.
These models generate plausible timelines from historical patterns; without calibration and auditing, their “probabilities” may not reflect reality.
BACKGROUND
Digital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials.
METHODS
We leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial.
RESULTS
Our approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention.
CONCLUSIONS
Optimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)
BACKGROUND
Digital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials.
METHODS
We leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial.
RESULTS
Our approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention.
CONCLUSIONS
Optimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)
Toward Stronger FDA Approval Standards for AI Medical Devices
This brief examines the FDA’s medical AI device approval process and urges policymakers to close the gaps created by the growth of AI-enabled healthcare.
This brief examines the FDA’s medical AI device approval process and urges policymakers to close the gaps created by the growth of AI-enabled healthcare.
Shana Lynch, HAI Head of Content and Associate Director of Communications, pointed out the "'era of AI evangelism is giving way to an era of AI evaluation,'" in her AI predictions piece, where she interviewed several Stanford AI experts on their insights for AI impacts in 2026.
Shana Lynch, HAI Head of Content and Associate Director of Communications, pointed out the "'era of AI evangelism is giving way to an era of AI evaluation,'" in her AI predictions piece, where she interviewed several Stanford AI experts on their insights for AI impacts in 2026.
Vision-based Estimation of MDS-UPDRS Gait Scoresfor Assessing Parkinson’s Disease Motor Severity
Parkinson’s disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments.
Parkinson’s disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments.
