Can Large Language Models Like ChatGPT Revolutionize Stereotactic Radiosurgery?

Abstract

Stereotactic radiosurgery (SRS) is a precise form of radiation therapy used to treat tumors. New technologies are continuously emerging to improve treatment planning and delivery. This review explores the potential of ChatGPT, a large language model (LLM), as a transformative tool in SRS. We discuss how ChatGPT could be used for tasks like treatment planning optimization, streamlining workflows, and even patient education. While acknowledging the limitations of current research and the need for rigorous validation, we highlight the potential of LLMs to enhance the efficiency and effectiveness of SRS.

Introduction

Stereotactic radiosurgery (SRS) delivers high doses of radiation therapy with pinpoint accuracy to treat tumors in the brain, spine, lungs, and other body parts. Optimizing treatment plans is crucial for maximizing tumor control while minimizing side effects. Artificial intelligence (AI) is making inroads into healthcare, and large language models (LLMs) like ChatGPT present exciting possibilities for SRS.

ChatGPT for Enhanced SRS Treatment Planning

SRS treatment planning is a complex process involving various steps. ChatGPT could potentially:

• Analyze Patient Data: ChatGPT can analyze vast amounts of patient data, including scans and medical history, to identify optimal treatment targets and potential risk factors.

• Optimize Treatment Parameters: LLMs can analyze historical data and clinical trials to recommend optimal radiation doses, fractionation schemes, and beam geometry for individual patients.

• Identify Potential Complications: By analyzing vast datasets, ChatGPT could help predict and mitigate potential side effects like radiation-induced toxicity.

ChatGPT: Streamlining Workflows and Beyond

Beyond treatment planning, ChatGPT could transform SRS workflows by:

• Automating Repetitive Tasks: Generating reports, summarizing complex data, and scheduling follow-up appointments are tasks ChatGPT could automate, freeing up valuable time for clinicians.

• Personalized Patient Education: ChatGPT can generate clear and concise educational materials tailored to individual patient needs and literacy levels.

• Research Collaboration: LLMs can analyze vast amounts of medical literature, summarizing findings and facilitating research collaboration in SRS.

Challenges and Considerations

Despite its promise, ChatGPT's application in SRS requires careful consideration:

• Limited Clinical Data: LLMs require extensive training data for reliable outputs. Currently, limited clinical data in SRS might restrict its effectiveness.

• Black Box Problem: The inner workings of LLMs are often opaque, making it difficult to understand how they arrive at their outputs. Transparency and explainability are crucial for trust in clinical decision-making.

• Ethical Considerations: Data privacy, potential biases within the training data, and ensuring responsible use of AI in patient care are all ethical considerations.

Conclusion

ChatGPT represents a novel tool with the potential to revolutionize SRS. Further research is needed to validate its effectiveness and integrate it responsibly into clinical workflows. By addressing the challenges and fostering collaboration between AI developers and healthcare professionals, we can unlock the true potential of LLMs for improved patient outcomes in SRS.