CoChat
Sign Up
Cited by 62Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions

Open access
Saenz, Agustina D., Mass General Brigham AI Governance Committee, McCoy, Thomas, Mantha, Allison Bryant, Martin, Robert, Damiano, Robert, Adair, Deborah, Heaney, David +15 more·2024·npj Digital Medicine
DOI 10.1038/s41746-024-01300-8PMC PMC11608363
Open this paper in CoChat — free account

Abstract

This report presents a comprehensive case study for the responsible integration of artificial intelligence (AI) into healthcare settings. Recognizing the rapid advancement of AI technologies and their potential to transform healthcare delivery, we propose a set of guidelines emphasizing fairness, robustness, privacy, safety, transparency, explainability, accountability, and benefit. Through a multidisciplinary collaboration, we developed and operationalized these guidelines within a healthcare system, highlighting a case study on ambient documentation to demonstrate the practical application and challenges of implementing generative AI in clinical environments. Our proposed framework ensures continuous monitoring, evaluation, and adaptation of AI technologies, addressing ethical considerations and enhancing patient care. This work contributes to the discourse on responsible AI use in healthcare, offering a blueprint for institutions to navigate the complexities of AI integration responsibly and effectively, thus promoting better, more equitable healthcare outcomes.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41746-024-01300-8

Cites

0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Saenz et al developed a system-wide governance model at Mass General Brigham, operationalizing responsible AI principles through a structured intake process, shadow deployments, and a real-world case study in generative AI documentation.

    “Similarly, Saenz et al developed a system-wide governance model at Mass General Brigham, operationalizing responsible AI (RAI) principles through a structured intake process, shadow deployments, and a real-world case study in generative AI documentation.”

    From Establishing and implementing a responsible artificial intelligence framework: a 1-year review (2025) · cites this paper as [4]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 10

  1. [2]Al-Antari, M. A., Han, S.-M. & Kim, T.-S. Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput. Methods Prog. Biomed. 196, 105584 (2020). 32554139 · DOI · Paper page
  2. [3]Dai, L. et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12, 3242 (2021). 34050158 PMC8163820 · DOI · Paper page
  3. [4]UrRehman, Z. et al. Effective lung nodule detection using deep CNN with dual attention mechanisms. Sci. Rep. 14, 3934 (2024). 38365831 PMC10873370 · DOI
  4. [6]Tran, K. A. et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 13, 1–17 (2021). 34579788 PMC8477474 · DOI · Paper page
  5. [7]Wang, M. et al. Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation. Lancet Digit. Health 2, e506–e515 (2020). 32984796 PMC7508506 · DOI
  6. [8]Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930–1940 (2023). 37460753 · DOI · Paper page
  7. [9]Boonstra, A., Versluis, A. & Vos, J. F. J. Implementing electronic health records in hospitals: a systematic literature review. BMC Health Serv. Res. 14, 1–24 (2014). 25190184 PMC4162964 · DOI
  8. [10]Feder, A. et al. Data augmentations for improved (large) language model generalization. In Proc. 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
  9. [11]Subbaswamy, A. & Saria, S. From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics 21, 345–352 (2020). 31742354
  10. [12]Omiye, J. A. et al. Large language models in medicine: the potentials and pitfalls: a narrative review. Ann. Intern. Med. 177, 210–220 (2024). 38285984 · DOI

Cited by

Sorted by citations

Authors
Saenz, A. D., Mass General Brigham AI Governance Committee, McCoy, T., Mantha, A. B., Martin, R., Damiano, R., Adair, D., Heaney, D., Sisodia, R., Park, L., Forsberg, R., Tuffy, G., Murphy, S., Dreyer, K., Jones, M., Cosier, H. J., Logan, M., Bundela, Y., Centi, A., Ting, D., You, J. G., Landman, A., & Mishuris, R. G.
Affiliations
Harvard University (US)
Corresponding author
Saenz, Agustina D.
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2024-11-30
Volume
7
Issue
1
Pages
348-348
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-024-01300-8
PMC
PMC11608363
OpenAlex
W4404876980
Cited by
62
References
13
Citations by year
2026: 40 · 2025: 21 · 2024: 1
FWCI
5.80
Type
journal article
Language
en
Update notices
correction (2025-01-30)
Open access
Licence
Locations
  • npj Digital Medicine (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
responsible AI, artificial intelligence in healthcare, AI guidelines, generative AI, clinical documentation, fairness, privacy, safety, transparency, explainability, accountability, AI ethics, AI governance, medical institutions

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review