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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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Conversational Guide for Cataract Surgery Complications: A Comparative Study of Surgeons versus Large Language

Sathishkumar Sundaramoorthy1, Vineet Ratra2, Vijay Shankar3

  • 1Department of Vitreoretinal Diseases, Medical Research Foundation, Sankara Nethralaya, Chennai, India.

Ophthalmic Epidemiology
|April 2, 2025
PubMed
Summary

Surgeons explained cataract surgery complications with more empathy and detail than a large language model (LLM) chatbot. While chatbots offer honesty, further training is needed for them to assist effectively in patient counseling.

Keywords:
Artificial intelligenceChatGPTcataract complicationschatbotconversational guideslarge language modelpatient counselling

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Area of Science:

  • Ophthalmology
  • Medical Education
  • Artificial Intelligence

Background:

  • Explaining surgical complications to patients requires clarity, objectivity, and empathy.
  • Large language models (LLMs) offer potential tools for medical communication, but their efficacy in patient counseling is under investigation.

Purpose of the Study:

  • To compare the responses of senior surgeons and an LLM-based chatbot in explaining common cataract surgery complications.
  • To evaluate the comprehension, readability, accuracy, completeness, honesty, and empathy of surgeon versus chatbot explanations.

Main Methods:

  • Ten common cataract surgery complication scenarios were presented to seven senior surgeons and a chatbot.
  • Responses were assessed by independent graders using validated indices for comprehension, readability, and language complexity.
  • Accuracy, completeness, honesty, and empathy were also quantitatively graded.

Main Results:

  • Surgeon explanations were significantly more readable (less complex) than chatbot responses (p < 0.001).
  • Chatbot responses were more detailed but generalized, lacking specific alternative measures.
  • Accuracy and completeness scores were similar between surgeons and the chatbot (p = 0.164).
  • Surgeons scored significantly higher for empathy (p = 0.041), while honesty scores were comparable.

Conclusions:

  • LLM-based chatbots provide detailed but less specific information and lower empathy compared to surgeons.
  • Surgeons demonstrate a more in-depth understanding and greater empathy in explaining complications.
  • Further specialized training for chatbots could enable them to assist surgeons in patient counseling for postoperative complications.