Evidence map›Paper›PMID 35969464›Full record

ArticleJMIR medical informatics2022

Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint.

Zoe Hu, Ricky Hu, Olivia Yau, Minnie Teng, Patrick Wang, Grace Hu, Rohit Singla

Abstract read
In one paragraph

Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

7 authors.

Zoe Hu *School of Medicine, Queen's University, Kingston, ON, Canada.ORCID https://orcid.org/0000-0001-7353-4490
Ricky Hu *School of Medicine, Queen's University, Kingston, ON, Canada.ORCID https://orcid.org/0000-0002-2843-3532
Olivia YauSchool of Medicine, University of British Columbia, Vancouver, BC, Canada.ORCID https://orcid.org/0000-0002-4866-8910
Minnie TengSchool of Medicine, University of British Columbia, Vancouver, BC, Canada.ORCID https://orcid.org/0000-0002-8819-6516
Patrick WangSchool of Medicine, Queen's University, Kingston, ON, Canada.ORCID https://orcid.org/0000-0002-0394-3878
Grace HuTemerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-8757-0281
Rohit SinglaSchool of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.ORCID https://orcid.org/0000-0002-8876-654X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.

Indexed as

AIartificial intelligencehealth care traineeshealth care workersmedical education

Identifiers

PMID35969464
PMCPMC9425164

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.