Evidence map›Paper›PMID 39906065›Full record

ArticleFrontiers in digital health2025

A roadmap to implementing machine learning in healthcare: from concept to practice.

Adam Paul Yan, Lin Lawrence Guo, Jiro Inoue, Santiago Eduardo Arciniegas, Emily Vettese, Agata Wolochacz, Nicole Crellin-Parsons, Brandon Purves, Steven Wallace, Azaz Patel and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. A pragmatic risk-stratified framework for using large language models in intensive care medicine: A narrative review.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2026
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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

14 authors.

Adam Paul Yan *Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON, Canada.
Lin Lawrence Guo *Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.
Jiro InoueProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.
Santiago Eduardo ArciniegasProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.
Emily VetteseProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.
Agata WolochaczProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.
Nicole Crellin-ParsonsProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.
Brandon PurvesInformation Management Technology, The Hospital for Sick Children, Toronto, ON, Canada.
Steven WallaceInformation Management Technology, The Hospital for Sick Children, Toronto, ON, Canada.
Azaz PatelInformation Management Technology, The Hospital for Sick Children, Toronto, ON, Canada.
Medhat RoshdiInformation Management Technology, The Hospital for Sick Children, Toronto, ON, Canada.
Karim JessaInformation Management Technology, The Hospital for Sick Children, Toronto, ON, Canada.
Bren CardiffInformation Management Technology, The Hospital for Sick Children, Toronto, ON, Canada.
Lillian SungDivision of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The adoption of machine learning (ML) has been slow within the healthcare setting. We launched Pediatric Real-world Evaluative Data sciences for Clinical Transformation (PREDICT) at a pediatric hospital. Its goal was to develop, deploy, evaluate and maintain clinical ML models to improve pediatric patient outcomes using electronic health records data. Objective: To provide examples from the PREDICT experience illustrating how common challenges with clinical ML deployment were addressed. Materials and methods: We present common challenges in developing and deploying models in healthcare related to the following: identify clinical scenarios, establish data infrastructure and utilization, create machine learning operations and integrate into clinical workflows. Results: We show examples of how these challenges were overcome and provide suggestions for pragmatic solutions while maintaining best practices. Discussion: These approaches will require refinement over time as the number of deployments and experience increase.

Indexed as

clinical prediction modelsclinical utilizationelectronic health recordsimplementationmachine learning

Identifiers

PMID39906065
PMCPMC11788154

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.