Evidence map›Paper›PMID 37901715›Full record

ArticleMeta-radiology2023

A Comprehensive Survey of ChatGPT: Advancements, Applications, Prospects, and Challenges.

Anam Nazir, Ze Wang

Abstract read
In one paragraph

Article in Meta-radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

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

41 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. GPTNeXt: Biomedical Image Classification Investigations.Diagnostics (Basel, Switzerland) · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Undergraduate Nursing Students' Perspectives on Artificial Intelligence in Academia.The Canadian journal of nursing research = Revue canadienne de recherche en sciences infirmieres · 2025
    Article
  12. Article
  13. Evaluating ChatGPT Responses to Questions about Thalassemia.Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion · 2025
    Article
  14. Article
  15. Article
  16. Review
  17. The Role of ChatGPT in Dermatology Diagnostics.Diagnostics (Basel, Switzerland) · 2025
    Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Anam NazirDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine. W 670 Baltimore St, HSF III, Room 1173, Baltimore, MD 21201.
Ze WangDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine. W 670 Baltimore St, HSF III, Room 1173, Baltimore, MD 21201.

Funding

QAQC Johns Hopkins Institute for Clinical and Translational ResearchUL1TR003098 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2019 to 2023
$57.7M
High Spatial and Temporal Resolution MRI Mapping of Oxygen Consumption in HumansP41EB029460 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Ravinder Reddy · 2021 to 2026
$7.6M
Academic Industrial Partnership on Advanced Perfusion MRIR01EB031080 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Yulin V Chang, JOHN A DETRE · 2022 to 2026
$2.7M
Diversity Supplement to Brain entropy mapping in Alzheimer's DiseaseR01AG070227 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI WANG, ZE · 2021 to 2024
$2.5M
Assessing ASL CBF as a biomarker for early Alzheimer's disease detection and disease progressionR01AG060054 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI WANG, ZE · 2019 to 2021
$1.7M
NCATS NIH HHS UL1 TR003098NIA NIH HHS R01 AG060054NIA NIH HHS R01 AG070227NIBIB NIH HHS P41 EB029460NIBIB NIH HHS R01 EB031080
6 · The paper itself

Abstract

Large Language Models (LLMs) especially when combined with Generative Pre-trained Transformers (GPT) represent a groundbreaking in natural language processing. In particular, ChatGPT, a state-of-the-art conversational language model with a user-friendly interface, has garnered substantial attention owing to its remarkable capability for generating human-like responses across a variety of conversational scenarios. This survey offers an overview of ChatGPT, delving into its inception, evolution, and key technology. We summarize the fundamental principles that underpin ChatGPT, encompassing its introduction in conjunction with GPT and LLMs. We also highlight the specific characteristics of GPT models with details of their impressive language understanding and generation capabilities. We then summarize applications of ChatGPT in a few representative domains. In parallel to the many advantages that ChatGPT can provide, we discuss the limitations and challenges along with potential mitigation strategies. Despite various controversial arguments and ethical concerns, ChatGPT has drawn significant attention from research industries and academia in a very short period. The survey concludes with an envision of promising avenues for future research in the field of ChatGPT. It is worth noting that knowing and addressing the challenges faced by ChatGPT will mount the way for more reliable and trustworthy conversational agents in the years to come.

Indexed as

ChatGPTContextual LearningGenerative Pre-trained Transformers (GPT)Human-Computer InteractionLarge Language Models (LLMs)Natural Language Processing (NLP)Trustworthy Conversational Agents

Identifiers

PMID37901715
PMCPMC10611551

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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.