Enhancing Radiology Reports with AI for Patient-Centered Care

Effective Communication in Patient-Centered Health Care

Effective communication is vital in patient-centered health care. It links patient experience, clinical safety, and effectiveness. Radiologists play a key role by communicating through radiologic reports.

The Role of LLMs in Radiology

Large Language Models (LLMs) have significantly impacted radiology. ChatGPT is a prominent example. It is widely adopted and has shown promise in generating concise and comprehensible radiologic reports.

Improving Report Readability

ChatGPT can create simplified radiologic summaries and patient-friendly reports. Simplified reports use medical terms for professionals, while patient-friendly ones avoid jargon. Both aim to improve comprehension.

Standardization and Data Extraction

LLMs convert unstructured data into structured formats, aiding standardization and data extraction. They enhance the readability and understandability of radiologic reports, benefiting radiologists and patients alike.

Assessing Comprehension and Accuracy

Studies show that ChatGPT-generated reports improve patient understanding. Non-physician raters demonstrated better comprehension with patient-friendly reports. Radiologists also valued the qualitative aspects of AI-generated content.

Addressing AI Limitations

However, artificial hallucinations and inappropriate language in generative AI models like ChatGPT remain concerns. Radiologists must identify and address these issues for accurate medical communication.

Future Research and Considerations

Future studies should explore other imaging modalities and involve diverse evaluator groups. Proper prompt engineering can further enhance AI-generated report quality and patient engagement.

The Promise of AI in Radiology

AI-generated reports can streamline radiology workflows and improve patient care. Oversight by radiologists ensures accuracy, making AI a valuable tool in medical settings.

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