Evidence map›Paper›PMID 41031076›Full record

ArticleArXiv2024

Leveraging Generative AI for Clinical Evidence Synthesis Needs to Ensure Trustworthiness.

Gongbo Zhang, Qiao Jin, Denis Jered McInerney, Yong Chen, Fei Wang, Curtis L Cole, Qian Yang, Yanshan Wang, Bradley A Malin, Mor Peleg and 4 more

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 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

5 · Who and what money

Authors and funding

14 authors.

Gongbo ZhangColumbia University, Department of Biomedical Informatics, New York, 10032, US.
Qiao JinNational Institutes of Health, National Library of Medicine, National Center for Biotechnology Information, Bethesda, 20894, US.
Denis Jered McInerneyNortheastern University, the Khoury College of Computer Sciences, Boston, 02115, US.
Yong ChenUniversity of Pennsylvania, Department of Biostatistics, Epidemiology and Informatics, Philadelphia, 19104, US.
Fei WangWeill Cornell Medicine, Department of Population Health Sciences, New York, 10065, US.
Curtis L ColeWeill Cornell Medicine, Department of Population Health Sciences, New York, 10065, US.
Qian YangCornell University, Computing and Information Science, Ithaca, 14853, US.
Yanshan WangUniversity of Pittsburgh, Department of Health Information Management, Pittsburgh, 15260, US.
Bradley A MalinVanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, 37203, US.
Mor PelegUniversity of Haifa, Department of Information Systems, Haifa, 3498838, Israel.
Byron C WallaceNortheastern University, the Khoury College of Computer Sciences, Boston, 02115, US.
Zhiyong LuNational Institutes of Health, National Library of Medicine, National Center for Biotechnology Information, Bethesda, 20894, US.
Chunhua WengColumbia University, Department of Biomedical Informatics, New York, 10032, US.
Yifan PengWeill Cornell Medicine, Department of Population Health Sciences, New York, 10065, US.

Funding

Clinical and Translational Science AwardUL1TR001873 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI REILLY, MUREDACH P · 2016 to 2025
$99.0M
Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical DataR01LM009886 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WENG, CHUNHUA · 2009 to 2020
$5.3M
ClinEX - Clinical Evidence Extraction, Representation, and AppraisalR01LM014344 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Yong Chen, Yifan Peng · 2023 to 2026
$2.7M
Closing the loop with an automatic referral population and summarization systemR01LM014306 · NLM · WEILL MEDICAL COLL OF CORNELL UNIV · PI Yifan Peng, Justin Frederick Rousseau · 2023 to 2026
$2.7M
NCATS NIH HHS UL1 TR001873NLM NIH HHS R01 LM009886NLM NIH HHS R01 LM014306NLM NIH HHS R01 LM014344
6 · The paper itself

Abstract

Evidence-based medicine promises to improve the quality of healthcare by empowering clinical decisions and practices with the best available evidence. The rapid growth of clinical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated evidence synthesis.

Indexed as

clinical evidence synthesisevidence-based medicinelarge language modelstrustworthy generative AI

Identifiers

PMID41031076
PMCPMC12478423

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.