ArticleFrontiers in artificial intelligence2025
Balancing accuracy and user satisfaction: the role of prompt engineering in AI-driven healthcare solutions.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Multidimensional evaluation of large language models on the AAP in-service examination: Assessing accuracy, calibration, and citation reliability.PLOS digital health · 2026Article
- Ontology-driven generation of parameters for health technology assessment models: a prompt engineering study.International journal of technology assessment in health care · 2026Article
- How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical Use.Research square · 2026Article
- Explainable multimodal AI and neuro-symbolic clinical decision support system for chronic eye disease management: a digital health implementation study.Frontiers in digital health · 2026Article
- Explainable artificial intelligence for postoperative analgesia risk stratification and clinical decision support in abdominal surgery.Frontiers in digital health · 2026Article
- Artificial Intelligence in Medicine: A Definition of Terms.Journal of community hospital internal medicine perspectives · 2026Article
- Large language model-driven time-series forecasting of financial network indicators.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence in fundus photography for type 2 diabetes: a scoping review of systemic biomarkers and multi-organ risk prediction.Frontiers in digital health · 2026Review
- Explainable Artificial Intelligence Framework for Predicting Treatment Outcomes in Age-Related Macular Degeneration.Sensors (Basel, Switzerland) · 2025Article
- An Explainable AI Framework for Corneal Imaging Interpretation and Refractive Surgery Decision Support.Bioengineering (Basel, Switzerland) · 2025Article
- Large language models in clinical trials: applications, technical advances, and future directions.BMC medicine · 2025Review
- Ethical implications of using general-purpose LLMs in clinical settings: a comparative analysis of prompt engineering strategies and their impact on patient safety.BMC medical informatics and decision making · 2025Article
- Integrating Artificial Intelligence and Precision Therapeutics for Advancing the Diagnosis and Treatment of Age-Related Macular Degeneration.Bioengineering (Basel, Switzerland) · 2025Review
- Quantitative evaluation of meibomian gland dysfunction via deep learning-based infrared image segmentation.Frontiers in artificial intelligence · 2025Article
- Large language models in the management of chronic ocular diseases: a scoping review.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The rapid evolution of the Internet of Things (IoT) and Artificial Intelligence (AI) has opened new possibilities for public healthcare. Effective integration of these technologies is essential to ensure precise and efficient healthcare delivery. This study explores the application of IoT-enabled, AI-driven systems for detecting and managing Dry Eye Disease (DED), emphasizing the use of prompt engineering to enhance system performance. Methods: A specialized prompt mechanism was developed utilizing OpenAI GPT-4.0 and ERNIE Bot-4.0 APIs to assess the urgency of medical attention based on 5,747 simulated patient complaints. A Bidirectional Encoder Representations from Transformers (BERT) machine learning model was employed for text classification to differentiate urgent from non-urgent cases. User satisfaction was evaluated through composite scores derived from Service Experiences (SE) and Medical Quality (MQ) assessments. Results: The comparison between prompted and non-prompted queries revealed a significant accuracy increase from 80.1% to 99.6%. However, this improvement was accompanied by a notable rise in response time, resulting in a decrease in SE scores (95.5 to 84.7) but a substantial increase in MQ satisfaction (73.4 to 96.7). These findings indicate a trade-off between accuracy and user satisfaction. Discussion: The study highlights the critical role of prompt engineering in improving AI-based healthcare services. While enhanced accuracy is achievable, careful attention must be given to balancing response time and user satisfaction. Future research should optimize prompt structures, explore dynamic prompting approaches, and prioritize real-time evaluations to address the identified challenges and maximize the potential of IoT-integrated AI systems in medical applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.