Clinician-AI Collaboration for Decision Support in Telemedicine

9 pages•Published: July 12, 2024

Ryan Harari, Nima Ahmadi, Shiva Pourfalatoun, Abdullah Al-Taweel and Hamid Shokoohi

Abstract

AI systems, particularly Large Language Models (LLMs), have the potential to improve telemedicine. However, there is a need to further investigate the effectiveness of AI decision support and clinician-AI collaboration in this context. This study examines the impact of AI-only and clinician-AI support systems on trust, acceptance, usability, and cognitive load in telemedicine scenarios. In a randomized controlled study, twenty non-medical participants were randomly assigned to receive support from an AI-only or clinician-AI decision support system during cardiopulmonary resuscitation (CPR) scenarios simulated in an augmented reality (AR) headset. We used ChatGPT 3, a widely used LLM, as the AI system. Participants' responses were measured using trust, acceptance, and usability questionnaires, as well as a wearable wristband to collect physiological data. The results show that the clinician-AI scenario was perceived as more useful compared to the ChatGPT-only scenario. The collaborative approach also led to higher heart rate variability (HRV) and lower LF/HF ratio, indicating potentially lower mental effort compared to ChatGPT-only. No significant differences were found in system usability scale (SUS) and electrodermal activity (EDA) levels between the scenarios. These findings highlight the importance of involving clinicians in AI- supported telemedicine. Further research should explore real-world applications to validate the preliminary results.

Keyphrases: ai, ar, chatgpt, clinician ai collaboration, technology acceptance, telemedicine, trust

In: Kenneth Baclawski, Michael Kozak, Kirstie Bellman, Giuseppe D'Aniello, Alicia Ruvinsky and Candida Da Silva Ferreira Barreto (editors). Proceedings of Conference on Cognitive and Computational Aspects of Situation Management 2023, vol 102, pages 81-89.

Links:	https://easychair.org/publications/paper/VQ1x
	https://doi.org/10.29007/9qxd

BibTeX entry

@inproceedings{CogSIMA2023:Clinician_AI_Collaboration_Decision,
  author    = {Ryan Harari and Nima Ahmadi and Shiva Pourfalatoun and Abdullah Al-Taweel and Hamid Shokoohi},
  title     = {Clinician-AI Collaboration for Decision Support in Telemedicine},
  booktitle = {Proceedings of Conference on Cognitive and Computational Aspects of Situation Management 2023},
  editor    = {Kenneth Baclawski and Michael Kozak and Kirstie Bellman and Giuseppe D'Aniello and Alicia Ruvinsky and Candida Da Silva Ferreira Barreto},
  series    = {EPiC Series in Computing},
  volume    = {102},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/VQ1x},
  doi       = {10.29007/9qxd},
  pages     = {81-89},
  year      = {2024}}

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