Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals

TL;DR

This paper presents a comprehensive catalog of 450 clinical NLP datasets and benchmarks, revealing a significant gap between AI benchmark coverage and the tasks clinicians prioritize for automation in healthcare.

Contribution

It systematically reviews and annotates clinical NLP datasets, and compares benchmark tasks with clinicians' desired automation targets, highlighting misalignments.

Findings

AI benchmarks lack clinical relevance and coverage.

Clinicians prioritize tasks not represented in current benchmarks.

Existing benchmarks do not address routine clinical documentation.

Abstract

Publicly accessible benchmarks that allow for assessing and comparing model performances are important drivers of progress in artificial intelligence (AI). While recent advances in AI capabilities hold the potential to transform medical practice by assisting and augmenting the cognitive processes of healthcare professionals, the coverage of clinically relevant tasks by AI benchmarks is largely unclear. Furthermore, there is a lack of systematized meta-information that allows clinical AI researchers to quickly determine accessibility, scope, content and other characteristics of datasets and benchmark datasets relevant to the clinical domain. To address these issues, we curated and released a comprehensive catalogue of datasets and benchmarks pertaining to the broad domain of clinical and biomedical natural language processing (NLP), based on a systematic review of literature and online resources. A total of 450 NLP datasets were manually systematized and annotated with rich metadata, such as targeted tasks, clinical applicability, data types, performance metrics, accessibility and licensing information, and availability of data splits. We then compared tasks covered by AI benchmark datasets with relevant tasks that medical practitioners reported as highly desirable targets for automation in a previous empirical study. Our analysis indicates that AI benchmarks of direct clinical relevance are scarce and fail to cover most work activities that clinicians want to see addressed. In particular, tasks associated with routine documentation and patient data administration workflows are not represented despite significant associated workloads. Thus, currently available AI benchmarks are improperly aligned with desired targets for AI automation in clinical settings, and novel benchmarks should be created to fill these gaps.