| ISSN |
1949-8470 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Review |
| Article Title |
Large language models and large concept models in radiology: Present challenges, future directions, and critical perspectives
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Suleman A Merchant, Neesha Merchant, Shaju L Varghese and Mohd Javed S Shaikh |
| ORCID |
|
| Funding Agency and Grant Number |
|
| Corresponding Author |
Suleman A Merchant, Chairman, Dean, MD, Professor, Department of Radiology, LTM Medical College and LTM General Hospital, Sion, Mumbai 400022, Maharashtra, India. suleman.a.merchant@gmail.com |
| Key Words |
Radiology artificial intelligence; Large language models; Large concept models; Medical imaging artificial intelligence; Artificial intelligence in healthcare; Multimodal artificial intelligence models; Explainable artificial intelligence; Artificial intelligence model limitations and challenges; Natural language processing in radiology; Conceptual reasoning in artificial intelligence |
| Core Tip |
Current capabilities, applications, limitations of Large language models (LLMs) in radiology artificial intelligence (AI). LLMs transformed radiology AI improved textual-analysis, workflow automation, clinical decision support. Challenges: Limited reasoning depth, inaccuracies. Transformative role of LLMs in radiology, their architectural foundations, clinical utility are discussed. LLM limitations like token-level processing, hallucinations, and challenges in clinical adoption. Exploring new paradigm large concept models, having conceptual reasoning and multimodal integration to enhance clinical accuracy and reliability. Ethical, regulatory, and explainability considerations for AI tools in healthcare also discussed and a balanced and forward-looking view on AI’s role in radiology, covering both current innovations and anticipated advances through large concept models. |
| Publish Date |
2025-11-27 02:16 |
| Citation |
<p>Merchant SA, Merchant N, Varghese SL, Shaikh MJS. Large language models and large concept models in radiology: Present challenges, future directions, and critical perspectives. <i>World J Radiol</i> 2025; 17(11): 114754</p> |
| URL |
https://www.wjgnet.com/1949-8470/full/v17/i11/114754.htm |
| DOI |
https://dx.doi.org/10.4329/wjr.v17.i11.114754 |
