Evidence map›Paper›PMID 41220512›Full record

ArticleJMA journal2025

Evaluating Conferences as a Tool to Improve Medical Artificial Intelligence Comprehension among Healthcare Professionals: A before and after Study.

Nafisa Islam, Abdel Rahman Osman, Laveinia Godfrey

Abstract read
In one paragraph

Article in JMA journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Nafisa IslamBarking, Havering and Redbridge University Hospitals NHS Trust Queen's Hospital, Romford, United Kingdom.
Abdel Rahman OsmanBarking, Havering and Redbridge University Hospitals NHS Trust Queen's Hospital, Romford, United Kingdom.
Laveinia GodfreySherwood Forest Hospitals NHS Foundation Trust King's Mill Hospital, Sutton in Ashfield, Nottinghamshire, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The integration of artificial intelligence (AI) in the United Kingdom's National Health Service may enhance patient care and alleviate systemic pressures. However, adoption of medical AI is challenged by limited educational access, low confidence among staff, and concerns regarding transparency and ethics. We evaluated the National Health Service National AI Conference on its impact on the understanding and attitudes of health care professionals regarding AI. Methods: A before-and-after study design was employed using anonymized surveys distributed to conference attendees. The survey assessed participants' roles, prior experience with medical AI, and perceptions of AI's risks, benefits, and applications. Using a 5-point Likert scale, responses were analyzed via Wilcoxon's signed-rank test, after trialing McNemar's test, with statistical significance defined as p < 0.05. Results: The survey was completed by 43 attendees. Most were clinical professionals (53.49%), with 65.12% having never attended a similar conference. Most participants (pre-conference: 69.77% vs. post-conference: 85.05%, p = 0.0868) understood the benefits and uses of AI, agreed that AI has the potential to improve patient care (93.02% vs. 95.35%, p = 1.00), were interested in pursuing a career in medical AI (62.79% vs. 67.44%, p = 0.824), and were concerned about the use of AI in health care (65.12% vs. 53.49%, p = 0.419). We observed an increase in understanding of AI after the conference among participants (p = 0.0367). Participant confidence and empowerment increased from 53.49% to 69.77% (p = 0.00319) and from 51.16% to 67.44% (p = 0.00596), respectively; these increases, alongside the increase in understanding of AI, reached statistical significance when analyzed using Wilcoxon's test, but not when dichotomized and analyzed with McNemar's test. Conclusions: Our conference may increase AI understanding, confidence, and empowerment among health care professionals, encouraging further research into targeted medical AI education. A national AI curriculum, transparent governance, and robust information technology infrastructure are recommended to support the adoption of AI internationally.

Indexed as

AIAI comprehensionAI curriculumconferencemedical AImedical education

Identifiers

PMID41220512
PMCPMC12598227

What Socratic 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 Socratic graph.