Evidence map›Paper›PMID 39319335›Full record

ArticleFrontiers in transplantation2024

Proceedings of the 2024 Transplant AI Symposium.

Sara Naimimohasses, Shaf Keshavjee, Bo Wang, Mike Brudno, Aman Sidhu, Mamatha Bhat

Abstract read
In one paragraph

Article in Frontiers in transplantation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Sara NaimimohassesDivision of Gastroenterology, Toronto General Hospital, Toronto, ON, Canada.
Shaf KeshavjeeDepartment of Innovation, University Health Network, Toronto, ON, Canada.
Bo WangDepartment of Laboratory Medicine and Pathobiology, The Temerty Centre for AI Research and Education in Medicine, Toronto, ON, Canada.
Mike BrudnoDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.
Aman SidhuDivision of Gastroenterology, Toronto General Hospital, Toronto, ON, Canada.
Mamatha BhatDivision of Gastroenterology, Toronto General Hospital, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With recent advancements in deep learning (DL) techniques, the use of artificial intelligence (AI) has become increasingly prevalent in all fields. Currently valued at 9.01 billion USD, it is a rapidly growing market, projected to increase by 40% per annum. There has been great interest in how AI could transform the practice of medicine, with the potential to improve all healthcare spheres from workflow management, accessibility, and cost efficiency to enhanced diagnostics with improved prognostic accuracy, allowing the practice of precision medicine. The applicability of AI is particularly promising for transplant medicine, in which it can help navigate the complex interplay of a myriad of variables and improve patient care. However, caution must be exercised when developing DL models, ensuring they are trained with large, reliable, and diverse datasets to minimize bias and increase generalizability. There must be transparency in the methodology and extensive validation of the model, including randomized controlled trials to demonstrate performance and cultivate trust among physicians and patients. Furthermore, there is a need to regulate this rapidly evolving field, with updated policies for the governance of AI-based technologies. Taking this in consideration, we summarize the latest transplant AI developments from the Ajmera Transplant Center's inaugural symposium.

Indexed as

artificial intelligenceclinical deploymentmachine learningprognostication modelstransplant medicine

Identifiers

PMID39319335
PMCPMC11421390

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.