๐Ÿง‘๐Ÿผโ€๐Ÿ’ป Research - April 18, 2026

Artificial intelligence in cancer survivorship: evaluating ChatGPT’s performance in rehabilitation-related queries.

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โšก Quick Summary

This study evaluated the performance of ChatGPT in addressing patient-centered queries related to cancer rehabilitation. The findings indicate a mean accuracy score of 3.93 on a 5-point scale, suggesting that while ChatGPT provides generally accurate responses, it often lacks critical details necessary for effective rehabilitation guidance.

๐Ÿ” Key Details

  • ๐Ÿ“Š Queries Evaluated: 15 patient queries across 8 domains of cancer rehabilitation
  • ๐Ÿงฉ Domains Covered: General exercise, fatigue, bone metastasis, neuropathy, balance and falls, breast cancer, women’s health, pediatric cancer
  • โš™๏ธ Technology Used: ChatGPT-4
  • ๐Ÿ† Performance Metrics: Mean accuracy score: 3.93 (SDโ€‰=โ€‰1.10), completeness score: 2.13 (SDโ€‰=โ€‰0.83), global quality score: 3.60 (SDโ€‰=โ€‰1.12)

๐Ÿ”‘ Key Takeaways

  • ๐Ÿค– ChatGPT shows promise as a tool for cancer rehabilitation education.
  • ๐Ÿ“ˆ Accuracy of responses was generally high, but completeness was lacking.
  • โš ๏ธ Critical Details such as exercise dosage and safety precautions were often omitted.
  • ๐Ÿ’ก AI-driven models can enhance patient access to rehabilitation information.
  • ๐Ÿ‘ฉโ€โš•๏ธ Professional Oversight is essential for safe and effective use of AI in healthcare.
  • ๐ŸŒ Study conducted by licensed physical therapists to ensure clinical relevance.
  • ๐Ÿ“š Evidence-based frameworks should guide the use of AI in patient education.

๐Ÿ“š Background

Cancer survivorship presents unique challenges, particularly in the realm of rehabilitation. Survivors often face a range of physical and emotional issues that require tailored support. The integration of artificial intelligence in healthcare, particularly in patient education, has the potential to bridge gaps in information access and enhance self-management strategies for survivors.

๐Ÿ—’๏ธ Study

The study aimed to assess how well ChatGPT could respond to patient queries related to cancer rehabilitation. A total of 15 queries were crafted by two licensed physical therapists, covering various aspects of rehabilitation. Each query was input into ChatGPT-4, and the responses were evaluated for accuracy, completeness, and overall quality based on established clinical guidelines.

๐Ÿ“ˆ Results

ChatGPT achieved a mean accuracy score of 3.93 on a 5-point scale, indicating that responses were generally accurate and relevant. However, the mean completeness score of 2.13 on a 3-point scale highlighted a significant gap in the detail of responses, particularly regarding exercise recommendations and safety measures. The global quality score of 3.60 further emphasizes the need for improvement in the contextual relevance of the information provided.

๐ŸŒ Impact and Implications

The findings suggest that AI-driven conversational models like ChatGPT could significantly improve access to rehabilitation information for cancer survivors. However, to maximize their effectiveness, these tools must be refined to ensure they provide comprehensive, accurate, and safe recommendations. The potential for AI to support self-management in cancer survivorship is promising, especially when used under professional guidance.

๐Ÿ”ฎ Conclusion

This study highlights the potential of AI in enhancing cancer rehabilitation education. While ChatGPT demonstrates a solid foundation in providing accurate responses, there is a clear need for further refinement to improve the completeness and safety of the information offered. Continued research and development in this area could lead to more effective tools for supporting cancer survivors in their rehabilitation journeys.

๐Ÿ’ฌ Your comments

What are your thoughts on the use of AI in cancer rehabilitation? Do you see it as a valuable resource or are there concerns that need to be addressed? ๐Ÿ’ฌ Share your insights in the comments below or connect with us on social media:

Artificial intelligence in cancer survivorship: evaluating ChatGPT’s performance in rehabilitation-related queries.

Abstract

PURPOSE: This study aimed to evaluate the performance of ChatGPT in responding to patient-centered queries related to cancer rehabilitation. It examined the accuracy, completeness, and overall quality of ChatGPT’s responses across major domains of cancer rehabilitation from a physical therapy perspective.
METHODS: Fifteen representative patient queries covering eight domains of cancer rehabilitation-including general exercise, fatigue, bone metastasis, neuropathy, balance and falls, breast cancer, women’s health, and pediatric cancer-were developed by two licensed physical therapists. Each query was input into ChatGPT-4, and responses were independently evaluated for accuracy, completeness, and global quality based on established clinical practice guidelines and expert consensus.
RESULTS: ChatGPT demonstrated a mean accuracy score of 3.93 (SDโ€‰=โ€‰1.10) on a 5-point scale, a mean completeness score of 2.13 (SDโ€‰=โ€‰0.83) on a 3-point scale, and a mean global quality score of 3.60 (SDโ€‰=โ€‰1.12) on a 5-point scale. Responses were generally accurate and clinically relevant but often lacked details regarding exercise dosage, safety precautions, and guideline-concordant recommendations.
CONCLUSION: ChatGPT shows potential as an accessible tool for general cancer rehabilitation education but requires refinement to ensure accuracy, contextual relevance, and safety.
IMPLICATIONS FOR CANCER SURVIVORS: AI-driven conversational models may improve patient access to rehabilitation information and self-management support when used under professional oversight and guided by evidence-based frameworks.

Author: [‘Hao J’, ‘Yao Z’, ‘Fan P’, ‘Remis A’, ‘Wu K’]

Journal: Support Care Cancer

Citation: Hao J, et al. Artificial intelligence in cancer survivorship: evaluating ChatGPT’s performance in rehabilitation-related queries. Artificial intelligence in cancer survivorship: evaluating ChatGPT’s performance in rehabilitation-related queries. 2026; 34:(unknown pages). doi: 10.1007/s00520-026-10681-3

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