Artificial Intelligence in Multilingual Interpretation and Radiology Assessment for Clinical Language Evaluation (AI-MIRACLE)

Author:

Khanna Praneet1ORCID,Dhillon Gagandeep2ORCID,Buddhavarapu Venkata3,Verma Ram4,Kashyap Rahul56ORCID,Grewal Harpreet7

Affiliation:

1. The University of Missouri-Kansas City School of Medicine, Kansas City, MO 64108, USA

2. Department of Internal Medicine, University of Maryland Baltimore Washington Medical Center, Glen Burnie, MD 21061, USA

3. Banner Baywood Medical Center, Banner Health, Mesa, AZ 85206, USA

4. Department of Sleep Medicine, Parkview Health System, Fort Wayne, IN 46845, USA

5. Department of Research, WellSpan Health, York, PA 17403, USA

6. Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, USA

7. Department of Radiology, College of Medicine, Florida State University, Pensacola, FL 32514, USA

Abstract

The AI-MIRACLE Study investigates the efficacy of using ChatGPT 4.0, a large language model (LLM), for translating and simplifying radiology reports into multiple languages, aimed at enhancing patient comprehension. The study assesses the model’s performance across the most spoken languages in the U.S., emphasizing the accuracy and clarity of translated and simplified radiology reports for non-medical readers. This study employed ChatGPT 4.0 to translate and simplify selected radiology reports into Vietnamese, Tagalog, Spanish, Mandarin, and Arabic. Hindi was used as a preliminary test language for validation of the questionnaire. Performance was assessed via Google form surveys distributed to bilingual physicians, which assessed the translation accuracy and clarity of simplified texts provided by ChatGPT 4. Responses from 24 participants showed mixed results. The study underscores the model’s varying success across different languages, emphasizing both potential applications and limitations. ChatGPT 4.0 shows promise in breaking down language barriers in healthcare settings, enhancing patient comprehension of complex medical information. However, the performance is inconsistent across languages, indicating a need for further refinement and more inclusive training of AI models to handle diverse medical contexts and languages. The study highlights the role of LLMs in improving healthcare communication and patient comprehension, while indicating the need for continued advancements in AI technology, particularly in the translation of low-resource languages.

Publisher

MDPI AG

Reference21 articles.

1. (2024, March 23). Introducing ChatGPT. Available online: https://openai.com/blog/chatgpt.

2. Radiology Gets Chatty: The ChatGPT Saga Unfolds;Grewal;Cureus,2023

3. (2024, August 13). Introducing Gemini: Our largest and Most Capable AI Model. Google. Available online: https://blog.google/technology/ai/google-gemini-ai/.

4. (2024, August 13). Introducing the Next Generation of Claude. Available online: https://www.anthropic.com/news/claude-3-family.

5. (2024, August 13). Try Bard and Share Your Feedback. Google. Available online: https://blog.google/technology/ai/try-bard/.

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