APLUS: A Python library for usefulness simulations of machine learning models in healthcare

Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discrimination

“…APLUS is a novel framework that simulates care management workflows to assess the usefulness of ML models for deployment and integration 37 . It models nearly any workflow by defining workflows as sets of discrete states and transitions.…”

Discrepancies Between Clearance Summaries and Marketing Materials of Software-Enabled Medical Devices Cleared by the US Food and Drug Administration

“…In essence, we need to evolve our unit of examination from the model to the model plus the care workflow it drives. 3,8 The study by Clark and colleagues 1 does not venture this deep into the field, and the FDA is constrained in its ability to fully assess the risks and benefits of AI-and ML-enabled devices in context. Without full and accurate disclosures of how devices work, device adopters and monitors, too, are hamstringed in critical ways.…”

“…To fully fathom the implications of the discrepancies that Clark and colleagues identify, it is necessary to examine the decisions that would be made (and the action that would be taken or withheld) based on the AI- and ML-enabled devices’ output. In essence, we need to evolve our unit of examination from the model to the model plus the care workflow it drives . The study by Clark and colleagues does not venture this deep into the field, and the FDA is constrained in its ability to fully assess the risks and benefits of AI- and ML-enabled devices in context.…”

Machine learning in transfusion medicine: A scoping review

“…Overall, the most common countries for identified studies are the United States (44), followed by China (16), Europe (12), and Canada (6). The range of sample sizes reported in the studies varied from 41 to more than 4 million (Table 1).…”

“…To be useful in practice, models need to be validated and integrated into a clinical workflow, where capacity constraints and users ignoring alerts may limit the impact of even a perfectly performing model. 12 The purpose of this review is to collate the breadth of literature of ML in transfusion medicine, describing current trends and capturing key methodological approaches, adding to the recognized need for up-to-date discussion of the challenges and potential solutions to the prospective implementation of ML in transfusion medicine. 13 2 | METHODS…”