Evidence map›Paper›PMID 38618354›Full record

ArticleCureus2024

Partnering With Technology: Advancing Laparoscopy With Artificial Intelligence and Machine Learning.

Taufiqa Reza, Syed Faqeer Hussain Bokhari

Abstract readEditorial
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 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. Clear Vision, Clear Savings: Enhancing Efficiency in Minimally Invasive Surgery.JSLS : Journal of the Society of Laparoendoscopic Surgeons
    Pooled it
  2. Review
  3. Review
  4. 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

2 authors.

Taufiqa RezaMedicine, Avalon University School of Medicine, Youngstown, USA.
Syed Faqeer Hussain BokhariSurgery, King Edward Medical University, Lahore, PAK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in optimizing laparoscopic surgery, offering innovative solutions to enhance surgical precision, efficiency, and safety. This editorial explores the potential role of AI/ML across the surgical continuum, including preoperative optimization, intraoperative assistance, and postoperative care. It outlines the benefits of laparoscopic surgery compared to traditional open procedures and identifies current challenges such as technical difficulty and human error. The editorial discusses how AI and ML technologies can address these challenges, including patient selection and risk stratification, surgical planning and simulation, and personalized medicine approaches. Moreover, it examines the role of AI/ML in intraoperative assistance, such as instrument tracking and guidance, real-time tissue analysis, and the detection of potential complications. Postoperative care and follow-up are also explored, highlighting the potential of AI/ML in monitoring patient recovery, predicting and preventing complications, and tailoring rehabilitation plans. Ethical concerns surrounding data privacy and security, the lack of transparency in decision-making, potential job displacement, and regulatory frameworks are discussed as challenges to the widespread adoption of AI/ML in laparoscopic surgery. Finally, potential areas for further research and exploration are outlined, emphasizing interdisciplinary collaboration and the need for transparent and accountable AI systems. Overall, this editorial provides insights into the challenges and opportunities in harnessing AI/ML technologies to optimize laparoscopic surgery and improve patient outcomes.

Indexed as

artificial intelligencelaparoscopic surgerymachine learningminimally invasive surgerypatient selectionpostoperative carereal-time assistancerisk stratificationsurgerysurgical planning

Identifiers

PMID38618354
PMCPMC11009890

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.