[HTML][HTML] Meeting the moment: addressing barriers and facilitating clinical adoption of artificial intelligence in medical diagnosis
Clinical diagnosis is essentially a data curation and analysis activity through which clinicians
seek to gather and synthesize enough pieces of information about a patient to determine …
seek to gather and synthesize enough pieces of information about a patient to determine …
GA4GH: International policies and standards for data sharing across genomic research and healthcare
Summary The Global Alliance for Genomics and Health (GA4GH) aims to accelerate
biomedical advances by enabling the responsible sharing of clinical and genomic data …
biomedical advances by enabling the responsible sharing of clinical and genomic data …
Systematic review of approaches to preserve machine learning performance in the presence of temporal dataset shift in clinical medicine
Objective The change in performance of machine learning models over time as a result of
temporal dataset shift is a barrier to machine learning-derived models facilitating decision …
temporal dataset shift is a barrier to machine learning-derived models facilitating decision …
[HTML][HTML] Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study
MP Sendak, W Ratliff, D Sarro, E Alderton… - JMIR medical …, 2020 - medinform.jmir.org
Background: Successful integrations of machine learning into routine clinical care are
exceedingly rare, and barriers to its adoption are poorly characterized in the literature …
exceedingly rare, and barriers to its adoption are poorly characterized in the literature …
Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): a …
Background Pythia is an automated, clinically curated surgical data pipeline and repository
housing all surgical patient electronic health record (EHR) data from a large, quaternary …
housing all surgical patient electronic health record (EHR) data from a large, quaternary …
[PDF][PDF] A path for translation of machine learning products into healthcare delivery
Despite enormous enthusiasm, machine learning models are rarely translated into clinical
care and there is minimal evidence of clinical or economic impact. New conference venues …
care and there is minimal evidence of clinical or economic impact. New conference venues …
Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission
Importance The ability to accurately predict in-hospital mortality for patients at the time of
admission could improve clinical and operational decision-making and outcomes. Few of …
admission could improve clinical and operational decision-making and outcomes. Few of …
Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data
Objective In applying machine learning (ML) to electronic health record (EHR) data, many
decisions must be made before any ML is applied; such preprocessing requires substantial …
decisions must be made before any ML is applied; such preprocessing requires substantial …
[HTML][HTML] APLUS: a Python library for usefulness simulations of machine learning models in healthcare
Despite the creation of thousands of machine learning (ML) models, the promise of
improving patient care with ML remains largely unrealized. Adoption into clinical practice is …
improving patient care with ML remains largely unrealized. Adoption into clinical practice is …
Enabling collaborative governance of medical AI
Medical artificial intelligence needs governance to ensure safety and effectiveness, not just
centrally (for example, by the US Food and Drug Administration) but also locally to account …
centrally (for example, by the US Food and Drug Administration) but also locally to account …