mlsanityData to 14 Oct 2026

GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset

arXiv 2310.18498 ↗First posted 27 Oct 2023eess.IV cs.CV cs.LGcited by 7

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Abstract (arXiv)

This technical report delves into the application of GPT-4 Vision (GPT-4V) in the nuanced realm of COVID-19 image classification, leveraging the transformative potential of in-context learning to enhance diagnostic processes.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 27 Oct 2023.

Builds on

14 of 25 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  5. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  6. 2016Densely Connected Convolutional Networkscited by 6,720
  7. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  8. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  9. 2014Going Deeper with Convolutionscited by 4,241
  10. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  11. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  12. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
2 more
  1. 2023How Many Demonstrations Do You Need for In-context Learning?cited by 52
  2. 2023Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Frameworkcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 4; 2026: 1.

7 papers.