Evidence map›Paper›PMID 39525111›Full record

ArticleCureus2024

Perceptions of Artificial Intelligence in Medicine Among Newly Graduated Interns: A Cross-Sectional Study.

Ali H Sanad, Aalaa S Alsaegh, Hasan M Abdulla, Abdulla J Mohamed, Ahmed Alqassab, Sayed Mohamed A Sharaf, Mohamed H Abdulla, Sawsan A Khadem

Abstract read
In one paragraph

Article in Cureus, 2024. 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. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  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

8 authors.

Ali H SanadFaculty of Medicine, Ain Shams University, Cairo, EGY.
Aalaa S AlsaeghFaculty of Medicine, Ain Shams University, Cairo, EGY.
Hasan M AbdullaFaculty of Medicine, Mansoura University, Mansoura, EGY.
Abdulla J MohamedFaculty of Medicine, First Moscow State Medical University, Moscow, RUS.
Ahmed AlqassabFaculty of Medicine, Mansoura University, Mansoura, EGY.
Sayed Mohamed A SharafFaculty of Medicine, Mansoura University, Mansoura, EGY.
Mohamed H AbdullaSchool of Medicine, Zhejiang University, Hangzhou, CHN.
Sawsan A KhademDepartment of Radiology, Salmaniya Medical Complex, Manama, BHR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Artificial intelligence (AI) is rapidly transforming the healthcare sector, enhancing clinical decision-making, improving patient outcomes, and streamlining operations. Despite its promise, the integration of AI raises important questions about ethical considerations, data privacy, and implications for healthcare professionals. Methods This cross-sectional study utilized an online survey to assess the perceptions of newly graduated interns applying to post-graduate programs under the Saudi Commission for Health Specialties. A total of 349 participants were recruited through social media and professional networks. The structured questionnaire included sections on demographic information, awareness of AI, perceived impacts, concerns, training experiences, and future perspectives. Data were analyzed using descriptive and inferential statistics. Results The participants (N=349) were predominantly aged 20-25 years (142, 40.7%) with a higher representation of females (215, 61.6%). Awareness levels varied, with 65 participants (18.6%) reporting not being familiar with AI while 146 participants (41.8%) identified as familiar. A majority perceived AI positively, believing it improves patient diagnosis (114, 32.7%) and reduces medical errors (129, 36.9%). However, significant concerns emerged regarding data privacy (140, 40.1%) and job displacement (110, 31.5%). Notably, 189 participants (54.2%) reported no formal training in AI, highlighting a gap in preparedness. Conclusions The study reveals a mix of optimism and concern among newly graduated interns regarding AI in medicine. There is a critical need for enhanced training and education on AI technologies within medical curricula to prepare future healthcare professionals adequately. Addressing the opportunities and challenges posed by AI can foster a collaborative healthcare environment that prioritizes patient care while maintaining the human element of practice.

Indexed as

ai in medicineartificial intelligencedata privacyhealthcarejob displacementmedical educationmedical internspatient careperceptions

Identifiers

PMID39525111
PMCPMC11549944

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.