ChatGPT-4–Driven Liver Ultrasound Radiomics Analysis: Diagnostic Value and Drawbacks in a Comparative Study
Despite slightly lower sensitivity than IDL, ChatGPT-4 demonstrated high feasibility for ultrasound radiomics, offering faster processing, high-throughput analysis, and automated multi-image evaluation. These findings support its potential integration into AI-driven imaging workflows, with further refinements needed to enhance feature reproducibility and diagnostic accuracy.
Authors
- Savvas Andronikou (ORCID: https://orcid.org/0000-0002-1015-9790)
- Sudha A. Anupindi (ORCID: https://orcid.org/0000-0002-7784-3108)
- Chandra M. Sehgal (ORCID: https://orcid.org/0000-0002-8811-1930)
- Shyam Sunder B. Venkatakrishna (ORCID: https://orcid.org/0000-0002-1803-1740)
- John H. Holmes (ORCID: https://orcid.org/0000-0003-2167-3602)
- Kassa Darge (ORCID: https://orcid.org/0000-0002-8989-6485)
- Laith R. Sultan (ORCID: https://orcid.org/0000-0002-2632-4164)
- Michael R. Acord (ORCID: https://orcid.org/0000-0001-8575-0983)
- Hansel J. Otero (ORCID: https://orcid.org/0000-0003-4626-0732)
Institutions
- Children's Hospital of Philadelphia (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-05-19
- DOI
- https://doi.org/10.2196/68144
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72