ReviewJSLS : Journal of the Society of Laparoendoscopic Surgeons
Artificial Intelligence in Medicine: A Specialty-Level Overview of Emerging AI Trends.
Review in JSLS : Journal of the Society of Laparoendoscopic Surgeons. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Inherited Epidermolysis Bullosa: Current Evidence, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature.Nutrients · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Artificial intelligence (AI) is a turning point in medical advancement. Despite the burgeoning research in this field, there exists a general lack of overview of where AI is being most utilized. This study reviews and describes techniques and trends of AI in the major medical specialties. Method: A literature search was conducted through PubMed in 2024 using two different search methods. Twenty-nine medical specialties were included, including all 24 major medical board specialties and five additional subspecialties. Results: There were 143,578 publications involving AI identified with most these (87%) published in the last ten years (124,206) and 52% (74,239) in the last two years. Radiology and Pathology publications were the largest cohorts, 18% (25,319) and 17% (23,828), respectively. Plastic Surgery (1,053), Hepatobiliary (662), and Allergy/Immunology (449) were the least published. There has been a 10,859% growth rate in annual publications across all medical specialties, with Ophthalmology and Preventative Medicine being the fastest-growing areas of research despite Radiology and Pathology being the most researched to date. Conclusion: This review underscores AI's profound impact on medical research, highlighting its significant growth and utilization across various specialties. AI's influence is most pronounced in Radiology and Pathology, but the substantial increase in publications in Ophthalmology and Preventative Medicine suggests new emerging areas of focus. The ongoing expansion of AI in medicine presents a promising horizon for addressing complex healthcare challenges, fostering a deeper and more comprehensive integration across all specialties.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.