Evidence map›Paper›PMID 40384926›Full record

ReviewiScience2025

Implementation framework for AI deployment at scale in healthcare systems.

Hassan Sami Adnan, Amitis Shidani, Lei Clifton, Clare R Bankhead, Rafael Perera-Salazar

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Liquid Metal-Enabled Soft Bioelectronics for Human Health Monitoring.Small (Weinheim an der Bergstrasse, Germany) · 2026
    Review
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
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

5 authors.

Hassan Sami AdnanNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Amitis ShidaniDepartment of Statistics, University of Oxford, Oxford, UK.
Lei CliftonNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Clare R BankheadNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Rafael Perera-SalazarNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) and digital health technologies are increasingly used in the medical field. Despite promises of leading the future of personalized medicine and better clinical outcomes, implementation of AI faces barriers for deployment at scale. We introduce a novel implementation framework that can facilitate digital health designers, developers, patient groups, policymakers, and other stakeholders, to co-create and solve issues throughout the life cycle of designing, developing, deploying, monitoring, and maintaining algorithmic models. This framework targets health systems that integrate multiple machine learning (ML) models with various modalities. This design thinking approach promotes clinical utility beyond model prediction, combining privacy preservation with clinical parameters to establish a reward function for reinforcement learning, ranking competing models. This allows leveraging explainable AI (xAI) methods for clinical interpretability. Governance mechanisms and orchestration platforms can be integrated to monitor and manage models. The proposed framework guides users toward human-centered AI design and developing AI-enhanced health system solutions.

Indexed as

Artificial intelligenceMachine learningPublic health

Identifiers

PMID40384926
PMCPMC12083986

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.