Evidence map›Paper›PMID 39498482›Full record

ReviewInternational journal of technology assessment in health care2024

Is health technology assessment ready for generative pretrained transformer large language models? Report of a fishbowl inquiry.

Clifford Goodman, Ellie Treloar

Abstract readReview
In one paragraph

Review in International journal of technology assessment in health care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Clifford GoodmanIndependent Consultant, Health Care Technology & Policy, Bethesda, MD, USA.ORCID https://orcid.org/0000-0002-7459-4994
Ellie TreloarDiscipline of Surgery, University of Adelaide, AdelaideSA, Australia.ORCID https://orcid.org/0000-0002-4840-8967

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe Health Technology Assessment International (HTAi) 2023 Annual Meeting included a novel "fishbowl" session intended to 1) probe the role of HTA in the emergence of generative pretrained transformer (GPT) large language models (LLMs) into health care and 2) demonstrate the semistructured, interactive fishbowl process applied to an emerging "hot topic" by diverse international participants.

methodsThe fishbowl process is a format for conducting medium-to-large group discussions. Participants are separated into an inner group and an outer group on the periphery. The inner group responds to a set of questions, whereas the outer group listens actively. During the session, participants voluntarily enter and leave the inner group. The questions for this fishbowl were: What are current and potential future applications of GPT LLMs in health care? How can HTA assess intended and unintended impacts of GPT LLM applications in health care? How might GPT be used to improve HTA methodology?

resultsParticipants offered approximately sixty responses across the three questions. Among the prominent themes were: improving operational efficiency, terminology and language, training and education, evidence synthesis, detecting and minimizing biases, stakeholder engagement, and recognizing and accounting for ethical, legal, and social implications.

conclusionsThe interactive fishbowl format enabled the sharing of real-time input on how GPT LLMs and related disruptive technologies will influence what technologies will be assessed, how they will be assessed, and how they might be used to improve HTA. It offers novel perspectives from the HTA community and aligns with certain aspects of ongoing HTA and evidence framework development.

Indexed as

Technology Assessment, BiomedicalHumansLanguageStakeholder Participationartificial intelligencegroup processestechnology assessment, biomedical

Identifiers

PMID39498482
PMCPMC11569908

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.