Opportunities and Challenges of Large Language Models in Medical Imaging
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Abstract
Large Language Models (LLMs) have the potential to revolutionize medical imaging by improving diagnostic accuracy, enhancing workflow efficiency, and advancing personalized medicine. However, addressing the challenges related to data privacy, hallucinations, interpretability, bias, and regulatory issues is crucial for the successful and ethical integration of LLMs into clinical practice. Collaboration between radiologists, AI developers, and other stakeholders is essential to ensure this technology benefits patients and healthcare providers
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21- Neetu Soni, Manish Ora, Amit Agarwal, Tianbao Yang, and Girish Bathla, "A Review of The Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead." American Journal of Neuroradiology, (2024).
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2- Dakai Jin et al., "Artificial intelligence in radiology." in Artificial Intelligence in Medicine: Elsevier, pp. 265-8, (2021).
3- Michael L Richardson et al., "Noninterpretive uses of artificial intelligence in radiology." Academic Radiology, Vol. 28 (No. 9), pp. 1225-35, (2021).
4- Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, and Hugo JWL Aerts, "Artificial intelligence in radiology." Nature Reviews Cancer, Vol. 18 (No. 8), pp. 500-10, (2018).
5- Koichiro Yasaka and Osamu Abe, "Deep learning and artificial intelligence in radiology: Current applications and future directions." PLoS medicine, Vol. 15 (No. 11), p. e1002707, (2018).
6- Hongjian Zhou et al., "A survey of large language models in medicine: Progress, application, and challenge." arXiv preprint arXiv:2311.05112, (2023).
7- Adib Bin Rashid and Ashfakul Karim Kausik, "AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications." Hybrid Advances, p. 100277, (2024).
8- Takeshi Nakaura et al., "Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports." Japanese Journal of Radiology, Vol. 42 (No. 2), pp. 190-200 ,(2024).
9- Zhaoyi Sun et al., "Evaluating GPT-4 on impressions generation in radiology reports." Radiology, Vol. 307 (No. 5), p. e231259, (2023).
10- Felix Busch et al., "Large language models for structured reporting in radiology: past, present, and future." European Radiology, pp. 1-14, (2024).
11- Lisa C Adams et al., "Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study." Radiology, Vol. 307 (No. 4), p. e230725, (2023).
12- Daiju Ueda et al., "Diagnostic performance of ChatGPT from patient history and imaging findings on the Diagnosis Please quizzes." Radiology, Vol. 308 (No. 1), p. e231040, (2023).
13- Koichiro Yasaka, Jun Kanzawa, Noriko Kanemaru, Saori Koshino, and Osamu Abe, "Fine-Tuned Large Language Model for Extracting Patients on Pretreatment for Lung Cancer from a Picture Archiving and Communication System Based on Radiological Reports." Journal of imaging informatics in medicine, (2024).
14- A. Mahdavi, M. Amanzadeh, and M. Hamedan, "The Role of Large Language Models in Modern Medical Education: Opportunities and Challenges." Shiraz E-Med J, Letter Vol. 25 (No. 5), p. e144847, (2024).
15- Augustin Lecler, Loïc Duron, and Philippe Soyer, "Revolutionizing radiology with GPT-based models: current applications, future possibilities and limitations of ChatGPT." Diagnostic and Interventional Imaging, Vol. 104 (No. 6), pp. 269-74, (2023).
16- Rajesh Bhayana, Satheesh Krishna, and Robert R Bleakney, "Performance of ChatGPT on a radiology board-style examination: insights into current strengths and limitations." Radiology, Vol. 307 (No. 5), p. e230582, (2023).
17- Rajesh Bhayana, Robert R Bleakney, and Satheesh Krishna, "GPT-4 in radiology: improvements in advanced reasoning." Radiology, Vol. 307 (No. 5), p. e230987, (2023).
18- Rajesh Bhayana, "Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications." Radiology, Vol. 310 1p. e232756, (2024).
19- Liyan Tang et al., "Evaluating large language models on medical evidence summarization." NPJ digital medicine, Vol. 6 (No. 1), p. 158 ,(2023).
20- Matthew Hill, "Hallucinating machines: Exploring the ethical implications of generative language models." Open Access Te Herenga Waka-Victoria University of Wellington, (2023).
21- Neetu Soni, Manish Ora, Amit Agarwal, Tianbao Yang, and Girish Bathla, "A Review of The Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead." American Journal of Neuroradiology, (2024).
22- Hanzhou Li et al., "Decoding radiology reports: potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports." Clinical Imaging, Vol. 101pp. 137-41, (2023).
23- Hanzhou Li, John T Moon, Saptarshi Purkayastha, Leo Anthony Celi, Hari Trivedi, and Judy W Gichoya, "Ethics of large language models in medicine and medical research." The Lancet Digital Health, Vol. 5 (No. 6), pp. e333-e35, (2023).
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How to Cite
1.
Tarighatnia A, Amanzadeh M, Hamedan M, Kiani Mobareke M, D. Nader N. Opportunities and Challenges of Large Language Models in Medical Imaging. Frontiers Biomed Technol. 2025;.
