Evidence map›Paper›PMID 40994707›Full record

ArticleNature machine intelligence2024

Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research.

Artem A Trotsyuk, Quinn Waeiss, Raina Talwar Bhatia, Brandon J Aponte, Isabella M L Heffernan, Devika Madgavkar, Ryan Marshall Felder, Lisa Soleymani Lehmann, Megan J Palmer, Hank Greely and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature machine intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

22 authors.

Artem A TrotsyukCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA.
Quinn WaeissCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-4880-3043
Raina Talwar BhatiaCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA.
Brandon J AponteCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA.
Isabella M L HeffernanCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA.
Devika MadgavkarCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA.
Ryan Marshall FelderCenter for Bioethics, Cleveland Clinic, Cleveland, OH, USA.ORCID 0000-0003-1306-4946
Lisa Soleymani LehmannHarvard T.H. Chan School of Public Health, Boston, MA, USA.
Megan J PalmerDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Hank GreelyStanford Law School, Stanford University, Stanford, CA, USA.
Russell WaldHuman-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA.
Lea GoetzArtificial Intelligence and Machine Learning, GSK, London, UK.
Markus TrengoveArtificial Intelligence and Machine Learning, GSK, London, UK.
Robert VandersluisArtificial Intelligence and Machine Learning, GSK, London, UK.
Herbert LinFreeman Spogli Institute for International Studies, Stanford University, Stanford, CA, USA.
Mildred K ChoCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA.
Russ B AltmanDepartment of Bioengineering, Stanford University, Stanford, CA, USA.ORCID 0000-0003-3859-2905
Drew EndyDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
David A RelmanFreeman Spogli Institute for International Studies, Stanford University, Stanford, CA, USA.ORCID 0000-0001-8331-1354
Margaret LeviFreeman Spogli Institute for International Studies, Stanford University, Stanford, CA, USA.
Debra SatzDepartment of Philosophy, Stanford University, Stanford, CA, USA.
David MagnusCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA.

Funding

Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
The Stanford Training Program in ELSI ResearchT32HG008953 · NHGRI · STANFORD UNIVERSITY · PI Mildred K. Cho, Meghan Halley · 2016 to 2026
$3.1M
Integrating Ethics into Machine Learning for Precision MedicineR01HG010476 · NHGRI · STANFORD UNIVERSITY · PI CHO, MILDRED K., SANKAR, PAMELA L · 2019 to 2021
$1.8M
NCATS NIH HHS UL1 TR003142NHGRI NIH HHS R01 HG010476NHGRI NIH HHS T32 HG008953
6 · The paper itself

Abstract

The rapid advancement of artificial intelligence (AI) in biomedical research presents considerable potential for misuse, including authoritarian surveillance, data misuse, bioweapon development, increase in inequity and abuse of privacy. We propose a multi-pronged framework for researchers to mitigate these risks, looking first to existing ethical frameworks and regulatory measures researchers can adapt to their own work, next to off-the-shelf AI solutions, then to design-specific solutions researchers can build into their AI to mitigate misuse. When researchers remain unable to address the potential for harmful misuse, and the risks outweigh potential benefits, we recommend researchers consider a different approach to answering their research question, or a new research question if the risks remain too great. We apply this framework to three different domains of AI research where misuse is likely to be problematic: (1) AI for drug and chemical discovery; (2) generative models for synthetic data; (3) ambient intelligence.

Identifiers

PMID40994707
PMCPMC12456743

What OpenQuestion holds

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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.