Evidence map›Paper›PMID 30617339›Full record

ReviewNature medicine2019

High-performance medicine: the convergence of human and artificial intelligence.

Eric J Topol

6 registry-linked trialsAbstract readReview
PubMed Publisher
In one paragraph

Review in Nature medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 6 registered trials, which are not on this map. Cited by 3,783 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3,783citing papers in PubMed, 8 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.

NCT03849040 completednot on this map

Development and Validation of a Computer-aided Algorithm Using Artificial Intelligence and Deep Neural Networks for the Segmentation of Ultrasonographic Features of Lymph Nodes During Endobronchial Ultrasound

Typeobservational_patient_registrySponsorSt. Joseph's Healthcare HamiltonRan2019 to 2019Enrolled52ConditionsLung Diseases, Lung NeoplasmArmsEndobronchial Ultrasound
NCT05689437 unknown statusnot on this mapstarted 2022, after this paper: background citation

MIRA Clinical Learning Environment (MIRACLE): Lung

TypeobservationalSponsorUniversity Health Network, TorontoRan2022 to 2023Enrolled1,000ConditionsLung CancerArmsApplication of ILD prediction machine learning model to planning imaging, Routine, automatic presentation of ILD risk level for evaluation by the clinician., Application of SGR machine learning model to diagnostic and planning imaging, Routine estimation of tumor specific growth rate (SGR) for lesions being considered for radiation therapy presented to clinician., Application of CBCT machine learning model to on-treatment imaging
NCT06232187 naunknown statusnot on this mapstarted 2024, after this paper: background citation

Scan-AId: Artificial Intelligence Support in Novice's Decision-making for Assessing Ultrasound Fetal Weight Estimation - A Randomized Trial

TypeinterventionalSponsorCopenhagen Academy for Medical Education and SimulationRan2024 to 2024Enrolled75ConditionsFetal Weight, UltrasoundArmsArtificial Intelligence feedback for ultrasound EFW standard plane images
NCT06726447 narecruitingnot on this mapstarted 2024, after this paper: background citation

sAItisfACES - Impact of an Artificial Intelligence Tool on the Satisfaction of Professionals and Patients in Ambulatory Consultations: A Randomized Cluster Study

TypeinterventionalSponsorUniversidad Autonoma de MadridRan2024 to 2026Enrolled148ConditionsGeneral Health StatusArmsArtificial Intelligence Tool
NCT07592039 completednot on this mapstarted 2021, after this paper: background citation

Research on Intelligent Optimization of Ventilator Parameters for Intensive Care Patients Based on Multimodal Large Models

TypeobservationalSponsorWu RongzhouRan2021 to 2025Enrolled2,000ConditionsAcute Respiratory Distress Syndrome, Pneumonia in Children, Respiratory Failure (Pediatric Patients)
NCT07727590 nanot yet recruitingnot on this mapstarted 2027, after this paper: background citation

Multimodal Visual Language Model-Assisted Diagnostic Strategy in the Emergency Department: A Prospective Multicenter Randomized Controlled Trial (ER-VISION-AI Study)

TypeinterventionalSponsorEwha Womans University Mokdong HospitalRan2027 to 2029Enrolled1,000ConditionsChest Pain, Dyspnea, Acute Cardiopulmonary Disease, Emergency Department PatientsArmsGenerative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support, Conventional emergency department diagnostic evaluation
3 · Its place in the literature

Who cites it

3,783 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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  10. Introducing Adolescents to the Social Dimensions of AI through Story-Driven Game-Based Learning.Artificial intelligence in education : 27th international conference, AIED 2026, Seoul, South Korea, June 27-July 3, 2026, proceedings. Part VI. International Conference on Artificial Intelligence in Education (27th : 2026 : Seoul, Kore... · 2027
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3,723 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Eric J TopolDepartment of Molecular Medicine, 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
NIH HHS UL1TR002550
6 · The paper itself

Abstract

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

Indexed as

Artificial IntelligenceMedicineAlgorithmsData AnalysisDeep LearningHumansPhysicians

Identifiers

PMID30617339

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.