Evidence map›Paper›PMID 37162073›Full record

SynthesisArquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery2023

FUTURE OF THE LANGUAGE MODELS IN HEALTHCARE: THE ROLE OF CHATGPT.

Francisco Tustumi, Nelson Adami Andreollo, José Eduardo de Aguilar-Nascimento

Abstract readSystematic Review
In one paragraph

Synthesis in Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Prompt engineering in medical education. Dissecting the new technological frontier in Digestive Surgery.Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery · 2026
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Exploring the role of artificial intelligence, large language models: Comparing patient-focused information and clinical decision support capabilities to the gynecologic oncology guidelines.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025
    Review
  15. Benefits, limits, and risks of ChatGPT in medicine.Frontiers in artificial intelligence · 2025
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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.

Francisco TustumiUniversidade de São Paulo, Faculty of Medicine, Department of Gastroenterology - São Paulo (SP), Brazil.ORCID http://orcid.org/0000-0001-6695-0496
Nelson Adami AndreolloUniversidade Estadual de Campinas, Faculty of Medical Sciences, Department of Surgery, Digestive Disease Surgical Unit - Campinas (SP), Brazil.ORCID http://orcid.org/0000-0001-7452-1165
José Eduardo de Aguilar-NascimentoUniversidade Federal de Mato Grosso, Medical School, Department of Surgery - Cuiabá (MT), Brazil.ORCID http://orcid.org/0000-0002-3583-6612

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The field of medicine has always been at the forefront of technological innovation, constantly seeking new strategies to diagnose, treat, and prevent diseases. Guidelines for clinical practice to orientate medical teams regarding diagnosis, treatment, and prevention measures have increased over the years. The purpose is to gather the most medical knowledge to construct an orientation for practice. Evidence-based guidelines follow several main characteristics of a systematic review, including systematic and unbiased search, selection, and extraction of the source of evidence. In recent years, the rapid advancement of artificial intelligence has provided clinicians and patients with access to personalized, data-driven insights, support and new opportunities for healthcare professionals to improve patient outcomes, increase efficiency, and reduce costs. One of the most exciting developments in Artificial Intelligence has been the emergence of chatbots. A chatbot is a computer program used to simulate conversations with human users. Recently, OpenAI, a research organization focused on machine learning, developed ChatGPT, a large language model that generates human-like text. ChatGPT uses a type of AI known as a deep learning model. ChatGPT can quickly search and select pieces of evidence through numerous databases to provide answers to complex questions, reducing the time and effort required to research a particular topic manually. Consequently, language models can accelerate the creation of clinical practice guidelines. While there is no doubt that ChatGPT has the potential to revolutionize the way healthcare is delivered, it is essential to note that it should not be used as a substitute for human healthcare professionals. Instead, ChatGPT should be considered a tool that can be used to augment and support the work of healthcare professionals, helping them to provide better care to their patients.

Indexed as

Artificial IntelligenceLanguageDelivery of Health CareHumansSoftware

Identifiers

PMID37162073
PMCPMC10168663

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.