Evidence map›Paper›PMID 36109635›Full record

ReviewNature medicine2022

Multimodal biomedical AI.

Julián N Acosta, Guido J Falcone, Pranav Rajpurkar, Eric J Topol

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 550 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
550citing papers in PubMed, 7 pooled it
–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

550 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  4. Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
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490 more citing papers are in PubMed but not listed here.

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

4 authors.

Julián N AcostaDepartment of Neurology, Yale School of Medicine, New Haven, CT, USA.ORCID http://orcid.org/0000-0001-6497-5454
Guido J FalconeDepartment of Neurology, Yale School of Medicine, New Haven, CT, USA.
Pranav RajpurkarDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. pranav_rajpurkar@hms.harvard.edu.ORCID http://orcid.org/0000-0002-8030-3727
Eric J TopolScripps Research Translational Institute, Scripps Research, La Jolla, CA, USA. etopol@scripps.edu.ORCID http://orcid.org/0000-0002-1478-4729

Funding

Scripps Translational Science InstituteUL1TR002550 · NCATS · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2018 to 2022
$28.9M
NCATS NIH HHS UL1 TR002550
6 · The paper itself

Abstract

The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimodal artificial intelligence solutions that capture the complexity of human health and disease. In this Review, we outline the key applications enabled, along with the technical and analytical challenges. We explore opportunities in personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, digital twin technology and virtual health assistants. Further, we survey the data, modeling and privacy challenges that must be overcome to realize the full potential of multimodal artificial intelligence in health.

Indexed as

Artificial IntelligencePandemicsElectronic Health RecordsHumansPrivacy

Identifiers

What OpenQuestion holds

Textmetadata
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.