Large Language Models Encode Radiation Oncology Domain Knowledge: Performance on the American College of Radiology Standardized Examination-Reference-Cited by-同舟云学术

Large Language Models Encode Radiation Oncology Domain Knowledge: Performance on the American College of Radiology Standardized Examination

Published:2024-02-01 Issue:1 Volume:1 Page:43-50
ISSN:2993-091X
Container-title:AI in Precision Oncology
language:en
Short-container-title:AI in Precision Oncology

Author:

Thaker Nikhil G.¹²^ORCID,Redjal Navid¹,Loaiza-Bonilla Arturo¹,Penberthy David³,Showalter Tim⁴,Choudhri Ajay¹,Williamson Shirnett¹,Thaker Gautam²,Shah Chirag⁵,Ward Matthew C.⁶,Thaker Mihir⁷,Arcaro Michael¹

Affiliation:

1. Division of Radiation Oncology, Capital Health, Pennington, New Jersey, USA.

2. Bayta Systems, Newtown, Pennsylvania, USA.

3. Department of Radiation Oncology, Penn State Health, Hershey, Pennsylvania, USA.

4. ArteraAI, Los Altos, California, USA.

5. Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA.

6. Division of Radiation Oncology, Atrium Health Levine Cancer Institute, Charlotte, North Carolina, USA.

7. Division of Urology, Kaiser Permanente, Santa Rosa, California, USA.

Publisher

Mary Ann Liebert Inc

Link

https://www.liebertpub.com/doi/pdf/10.1089/aipo.2023.0007

Reference13 articles.

1. Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions

2. The rise of ChatGPT: Exploring its potential in medical education

3. The next paradigm shift? ChatGPT, artificial intelligence, and medical education

4. Results and Resident Evaluation of the 2007 American College of Radiology In-Training Examination in Radiation Oncology

5. Large language models encode clinical knowledge

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