Evidence map›Paper›PMID 40819118›Full record

ArticleCommunications medicine2025

Achieving inclusive healthcare through integrating education and research with AI and personalized curricula.

Amir Bahmani, Kexin Cha, Arash Alavi, Amit Dixit, Antony Ross, Ryan Park, Francesca Goncalves, Shirley Ma, Paul Saxman, Ramesh Nair and 23 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

33 authors.

Amir Bahmani *Stanford Deep Data Research Center, Stanford University, Stanford, CA, USA. abahman@stanford.edu.ORCID http://orcid.org/0000-0003-4533-9334
Kexin Cha *Stanford Deep Data Research Center, Stanford University, Stanford, CA, USA.
Arash Alavi *Stanford Deep Data Research Center, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-7245-6889
Amit Dixit *Stanford Deep Data Research Center, Stanford University, Stanford, CA, USA.
Antony RossStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0009-0004-5318-5405
Ryan ParkStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-7426-2068
Francesca GoncalvesStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.
Shirley MaStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.
Paul SaxmanGates Foundation, Seattle, WA, USA.
Ramesh NairDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-1323-3247
Ramin Akhavan-SarrafStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.
Xin ZhouStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-8089-4507
Meng WangDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-5672-1411
Kévin ContrepoisDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-9678-5161
Jennifer Li-Pook-ThanDepartment of Genetics, Stanford University, Stanford, CA, USA.
Emma MonteDepartment of Genetics, Stanford University, Stanford, CA, USA.
David Jose Florez RodriguezStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.
Jaslene LaiStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.
Mohan BabuStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-5482-2622
Abtin TondarStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.
Sophia Miryam Schüssler-Fiorenza RoseDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-6311-6671
Ilya AkbariStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.
Xinyue ZhangDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-6093-6335
Kritika YegnashankaranCenter for Teaching and Learning, Stanford University, Stanford, CA, USA.
Joseph YrachetaNative BioData Consortium, Eagle Butte, SD, USA.ORCID http://orcid.org/0000-0002-0691-8504
Kali DaleNative BioData Consortium, Eagle Butte, SD, USA.ORCID http://orcid.org/0009-0001-0558-7344
Alison Derbenwick MillerStanford Deep Data Research Center, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0009-0007-5358-1397
Scott EdmistonOffice of the Vice Provost and Dean of Research, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-1001-8314
Eva M McGheeMartin Luther King Jr. Community Healthcare Hospital, Los Angeles, CA, USA.
Camille NebekerHerbert Wertheim School of Public Health and Human Longevity Science, UC San Diego, San Diego, CA, USA.ORCID http://orcid.org/0000-0001-6819-1796
Joseph C WuStanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-6068-8041
Anshul KundajeDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-3084-2287
Michael SnyderDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-0784-7987

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrecision medicine promises significant health benefits but faces challenges such as complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) such as Generative Pretrained Transformer (GPT) and Claude highlights the importance of making complex data accessible to non-specialists.

methodsWe evaluated the Stanford Data Ocean (SDO) precision medicine training program's learning outcomes, AI Tutor performance, and learner satisfaction by assessing self-rated competency on key learning objectives through pre- and post-learning surveys, along with formative and summative assessment completion rates. We also analyzed AI Tutor accuracy and learners' self-reported satisfaction, and post-program academic and career impacts. Additionally, we demonstrated the capabilities of the AI Data Visualization tool.

resultsSDO demonstrates the ability to improve learning outcomes for learners from broad educational and socioeconomic backgrounds with the support of the AI Tutor. The AI Data Visualization tool enables learners to interpret multi-omics and wearable data and replicate research findings.

conclusionsSDO strives to mitigate challenges in precision medicine through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning. SDO provides AI Tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible to users from broad educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.

Identifiers

PMID40819118
PMCPMC12357942

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.