ArticleEuropean journal of investigation in health, psychology and education2024
Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care.
Article in European journal of investigation in health, psychology and education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Automated Discharge Instructions in Medical and Surgical Care: A Systematic Review of Patient Engagement and Clinical Outcomes.Healthcare (Basel, Switzerland) · 2026Review
- Artificial intelligence analysis of minimally invasive surgery data.Journal of robotic surgery · 2026Review
- Comparative analysis of artificial intelligence platforms in generating Post-Operative instructions for endoscopic transnasal skull base surgery.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Artificial Intelligence Physician Avatars for Patient Education: A Pilot Study.Journal of clinical medicine · 2025Article
- Assessing the Efficacy of Ortho GPT: A Comparative Study with Medical Students and General LLMs on Orthopedic Examination Questions.Bioengineering (Basel, Switzerland) · 2025Article
- The Development and Evaluation of a Retrieval-Augmented Generation Large Language Model Virtual Assistant for Postoperative Instructions.Bioengineering (Basel, Switzerland) · 2025Article
- Assessing the Accuracy of Diagnostic Capabilities of Large Language Models.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluation of artificial intelligence robot's knowledge and reliability on dental implants and peri-implant phenotype.Scientific reports · 2025Article
- Article
- ChatGPT as an Information Source for Patients with Migraines: A Qualitative Case Study.Healthcare (Basel, Switzerland) · 2024Article
- Large language models in patient education: a scoping review of applications in medicine.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA's responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.
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Registered trials
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