Assessing ChatGPT's Proficiency in Simplifying Radiological Reports for Healthcare Professionals and Patients

Pradosh Kumar Sarangi; Amrita Lumbani; M Sarthak Swarup; Suvankar Panda; Smruti Snigdha Sahoo; Pratisruti Hui; Anish Choudhary; Sudipta Mohakud; Ranjan Kumar Patel; Himel Mondal

doi:10.7759/cureus.50881

Assessing ChatGPT's Proficiency in Simplifying Radiological Reports for Healthcare Professionals and Patients

Cureus. 2023 Dec 21;15(12):e50881. doi: 10.7759/cureus.50881. eCollection 2023 Dec.

Authors

Affiliations

¹ Radiodiagnosis, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.
² Physiology, Mayo Institute of Medical Sciences, Barabanki, IND.
³ Radiodiagnosis, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND.
⁴ Radiodiagnosis, SCB (Srirama Chandra Bhanja) Medical College and Hospital, Cuttack, IND.
⁵ Radiodiagnosis, All India Institute of Medical Sciences, Kalyani, Kalyani, IND.
⁶ Radiodiagnosis, Central Institute of Psychiatry, Ranchi, IND.
⁷ Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND.
⁸ Physiology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND.

Abstract

Background Clear communication of radiological findings is crucial for effective healthcare decision-making. However, radiological reports are often complex with technical terminology, making them challenging for non-radiology healthcare professionals and patients to comprehend. Large language models like ChatGPT (Chat Generative Pre-trained Transformer, by OpenAI, San Francisco, CA) offer a potential solution by translating intricate reports into simplified language. This study aimed to assess the capability of ChatGPT-3.5 in simplifying radiological reports to facilitate improved understanding by healthcare professionals and patients. Materials and methods Nine radiological reports were taken for this study spanning various imaging modalities and medical conditions. These reports were used to ask ChatGPT a set of seven questions (describe the procedure, mention the key findings, express in a simple language, suggestions for further investigation, need of further investigation, grammatical or typing errors, and translation into Hindi). A total of eight radiologists rated the generated content in detailing, summarizing, simplifying content and language, factual correctness, further investigation, grammatical errors, and translation to Hindi. Results The highest score was obtained for detailing the report (94.17% accuracy) and the lowest score was for drawing conclusions for the patient (85% accuracy); case-wise scores were similar (p-value = 0.97). The Hindi translation by ChatGPT was not suitable for patient communication. Conclusion The current free version of ChatGPT-3.5 was able to simplify radiological reports effectively, removing technical jargon while preserving essential diagnostic information. The free version adeptly simplifies radiological reports, enhancing accessibility for healthcare professionals and patients. Hence, it has the potential to enhance medical communication, facilitating informed decision-making by healthcare professionals and patients.

Keywords: artificial intelligence; chatgpt; health education; health literacy; healthcare communication; hindi translation; large lqnguage model; natural language processing; patient-centered care; radiological report.