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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Related Experiment Video

Updated: May 24, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

How Good Are Patient-Facing LLMs at Survivorship Questions? A Comparative User Evaluation Across Four Chatbots.

Saif Khairat1, Safoora Masoumi1, Hanna Mehraby1

  • 1University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Cancer survivors want trustworthy AI chatbots for support between visits. Key needs include accuracy, empathy, and clear escalation paths to clinicians for safe integration into survivorship care.

Keywords:
cancer survivorshipgenerative AIlarge language modelssatisfactionuser evaluation

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Area of Science:

  • Oncology
  • Digital Health
  • Human-Computer Interaction

Background:

  • Cancer survivors increasingly use AI chatbots for support between clinical visits.
  • The safety and effectiveness conditions for these AI tools in survivorship care are not well-defined.

Purpose of the Study:

  • To understand cancer survivors' perceptions of AI chatbot (large language model - LLM) responses.
  • To establish requirements for the safe and effective deployment of LLM-based tools in cancer survivorship.

Main Methods:

  • Semi-structured interviews with 21 breast and prostate cancer survivors post-rating task.
  • Dual-coding of interview transcripts and thematic analysis to identify key themes.

Main Results:

  • Survivors' willingness to use AI was governed by trust, accuracy, provenance, personalization, empathy, usability, and escalation.
  • Preferred features included citation-first answers, light personalization, plain language instructions, and one-tap clinician escalation.

Conclusions:

  • Patient-defined requirements for AI in survivorship care align with established European priorities for trustworthy AI.
  • These findings can guide the development of integrated and reliable AI solutions for cancer survivors.