LLM research on public biosignals data is needed to protect patients.

James Anibal, Jasmine Gunkel, Hannah Huth, Bradford J Wood

Abstract readReview

In one paragraph

Review in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Large Language Models for Optimizing Patient Recruitment Decisions in Voice Data Generation Projects.Laryngoscope investigative otolaryngology · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

James AnibalCenter for Interventional Oncology, NIH Clinical Center, NIH, Bethesda, MD, USA. anibal.james@nih.gov.

Jasmine GunkelDepartment of Bioethics, NIH Clinical Center, NIH, Bethesda, MD, USA.

Hannah HuthCenter for Interventional Oncology, NIH Clinical Center, NIH, Bethesda, MD, USA.

Bradford J WoodCenter for Interventional Oncology, NIH Clinical Center, NIH, Bethesda, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large language models (LLMs) have expanded the capabilities of AI and created new opportunities in medicine. To promote the development of safe and innovative digital health tools, there must be public biosignals datasets that encourage research and provide benchmarks. Multiple current examples involve data use agreements that restrict LLM studies. This work presents recommendations to ensure that public releases of biosignals data can ethically facilitate LLM research efforts.

Identifiers

PMID42401685

DOI10.1109/OJSP.2023.3344375

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.