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Could generative artificial intelligence replace fieldwork in pain research?

Suzana Bojic1,2, Nemanja Radovanovic3, Milica Radovic4

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Generative artificial intelligence (AI) models struggle to accurately capture acute pain data in rock climbers compared to human reporting. These AI models also produced unreliable, untraceable references, raising significant data integrity concerns.

Keywords:
acute painartificial intelligenceclimbinggenerative pretrained transformer

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Area of Science:

  • Sports Medicine
  • Artificial Intelligence
  • Pain Research

Background:

  • Generative artificial intelligence (AI) models show promise for data acquisition in pain research.
  • Concerns exist regarding the accuracy and reliability of AI-generated data.
  • This study compares AI data acquisition for acute pain in rock climbers against field research.

Purpose of the Study:

  • To evaluate the capacity of open generative AI models in acquiring acute pain data from rock climbers.
  • To compare AI-generated pain data with data collected through traditional field research methods.
  • To assess the reliability and accuracy of AI models in pain research.

Main Methods:

  • Fifty-two rock climbers reported pain location and intensity during a climbing session.
  • Five generative pretrained transformer models were used to answer the same pain-related questions.
  • AI-generated responses were compared against the climbers' self-reported data.

Main Results:

  • Rock climbers reported pain primarily in the forearm and toes.
  • AI models identified different primary pain sites (fingers, hands, shoulders, legs, feet).
  • Two AI models provided untraceable references, indicating potential data fabrication.

Conclusions:

  • Open generative AI models currently cannot match the quality of field-collected pain data in this population.
  • The generation of non-existent references by AI models raises serious concerns about their reliability in research.
  • Further development is needed to improve the accuracy and trustworthiness of AI in pain research.