Evidence map›Paper›PMID 41911411›Full record

ArticleInteractive journal of medical research2026

An Introduction to AI for Clinicians: Tutorial.

Stephen B Lee, Alexis B Carter, Muhammad Hamis Haider, Seok-Bum Ko

Abstract read
In one paragraph

Article in Interactive journal of medical research, 2026. 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

4 authors.

Stephen B LeeDivision of Infectious Diseases, University of Saskatchewan, 1440-14th Avenue, Regina General Hospital, 2nd Floor Medical Office Wing, ID Clinic, Regina, SK, S4P 0W5, Canada, 1 3067664247.ORCID http://orcid.org/0000-0002-6253-293X
Alexis B CarterDepartment of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, United States.ORCID http://orcid.org/0000-0002-0171-2216
Muhammad Hamis HaiderDepartment of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada.ORCID http://orcid.org/0000-0003-3124-7842
Seok-Bum KoDepartment of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada.ORCID http://orcid.org/0000-0002-9287-317X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Artificial intelligence (AI) is already fundamentally changing society, with medicine being no exception. AI will impact how we practice, how hospitals operate, and even the practice of medicine itself. The use of AI-based products has already begun, with examples including AI scribes and large language models such as ChatGPT. Work is ongoing to produce models that have specific functions within medicine, such as kidney injury prediction. However, transformative foundational work, such as AlphaFold (for protein structure prediction), also promises to completely change the way we approach medicine. Therefore, clinicians must develop a clear understanding of AI, not as an optional skill, but as a core competency of modern medical practice. This paper serves as a tutorial to guide medical professionals through the basic principles of AI. It will teach clinicians how to build a mental scaffold to understand and springboard into AI. The core parts of this paper are organized in steps, with additional relevant topics addressed in modules at the end of the paper. The core steps are meant to be read sequentially. To prepare the reader for the rest of the paper, this tutorial will first introduce what AI is and then cover some basic definitions needed to understand other concepts. The reader will then be ready to understand what deep learning is and the difference between supervised and unsupervised learning. Finally, the reader will go through how deep learning models learn. Separate modules on safety and clinical applications are also included. This tutorial is relevant to clinicians at all levels but may be particularly useful for practicing clinicians who are encountering AI tools integrated into their practices without previous formal education in the field. Users of this tutorial can refer to specific sections or read the entire paper.

Indexed as

AIartificial intelligencedeep learningmachine learningmedical education

Identifiers

PMID41911411
PMCPMC13035078

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.