Evidence map›Paper›PMID 42254133›Full record

ReviewAnnals of medicine and surgery (2012)2026

Artificial intelligence in gastrointestinal and hepatopancreatobiliary surgery: innovations, integration, and future directions.

Vidit Amitbhai Dholakia, Suvendu Sekhar Jena, Samiran Nundy

Abstract readReview
In one paragraph

Review in Annals of medicine and surgery (2012), 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

3 authors.

Vidit Amitbhai DholakiaInstitute of Surgical Gastroenterology, GI & HPB Onco-Surgery and Liver Transplantation, Sir Ganga Ram Hospital, New Delhi, India.ORCID https://orcid.org/0000-0001-9161-1971
Suvendu Sekhar JenaInstitute of Surgical Gastroenterology, GI & HPB Onco-Surgery and Liver Transplantation, Sir Ganga Ram Hospital, New Delhi, India.
Samiran NundyInstitute of Surgical Gastroenterology, GI & HPB Onco-Surgery and Liver Transplantation, Sir Ganga Ram Hospital, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is rapidly emerging as a transformative force in gastrointestinal (GI) and hepatopancreatobiliary (HPB) surgery, with the potential to enhance decision-making and surgical precision across the entire perioperative continuum. This narrative review explores the current applications and future directions of AI in GI and HPB surgical practice. We evaluated validated AI tools and platforms used in diagnostics, preoperative planning, intraoperative guidance, postoperative monitoring, and surgical education, while also considering the associated ethical and infrastructural challenges. AI-driven risk stratification models such as MySurgeryRisk and the POTTER calculator are improving preoperative prediction of complications. In endoscopy, systems like GI Genius have demonstrated improved adenoma detection rates, while EndoBRAIN-Plus enables real-time histologic assessment using high-resolution endocytoscopy. For surgical planning, virtual hepatectomy and three-dimensional reconstruction platforms such as HepaVision facilitate personalized and function-oriented liver resections. Intraoperatively, augmented reality and computer vision technologies provide enhanced anatomical visualization and guidance during minimally invasive procedures. AI-based predictive models are also contributing to improved postoperative monitoring by identifying patients at risk of complications such as anastomotic leaks. Furthermore, AI-driven simulation platforms are increasingly being integrated into surgical training and education. Overall, AI should be viewed not as a replacement for surgeons but as a powerful tool that can augment clinical judgment and improve surgical safety and personalization. However, challenges related to data standardization, model transparency, ethical governance, and clinical integration remain important barriers to widespread adoption.

Indexed as

artificial intelligencegastrointestinal surgeryhepatobiliary surgerymachine learningsurgical innovation

Identifiers

PMID42254133
PMCPMC13236362

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.