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| http://dx.doi.org/10.1002/jdd.13479 | DOI Listing |
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Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review.
J Pers Med
December 2024
Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark.
Artificial intelligence (AI) is becoming increasingly influential in ophthalmology, particularly through advancements in machine learning, deep learning, robotics, neural networks, and natural language processing (NLP). Among these, NLP-based chatbots are the most readily accessible and are driven by AI-based large language models (LLMs). These chatbots have facilitated new research avenues and have gained traction in both clinical and surgical applications in ophthalmology.
View Article and Find Full Text PDFTwitter users perceptions of AI-based e-learning technologies.
Sci Rep
March 2024
Department of Statistics, University of Bologna, 40126, Bologna, Italy.
Today, teaching and learning paths increasingly intersect with technologies powered by emerging artificial intelligence (AI).This work analyses public opinions and sentiments about AI applications that affect e-learning, such as ChatGPT, virtual and augmented reality, microlearning, mobile learning, adaptive learning, and gamification. The way people perceive technologies fuelled by artificial intelligence can be tracked in real time in microblog messages promptly shared by Twitter users, who currently constitute a large and ever-increasing number of individuals.
View Article and Find Full Text PDFAn AI-based e-learning tool to improve endodontic diagnostics in undergraduate students.
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Department of Periodontology and Operative Dentistry, University of Münster, Münster, Germany.
Applying AI and Guidelines to Assist Medical Students in Recognizing Patients With Heart Failure: Protocol for a Randomized Trial.
JMIR Res Protoc
October 2023
Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, United States.
Article Synopsis
- The integration of artificial intelligence (AI) in healthcare aims to enhance clinical practices like diagnosing conditions such as heart failure (HF) and tailoring treatments, but the responsible use of these systems is crucial to avoid bias and ensure effective outcomes.*
- This study focuses on improving the recognition of heart failure in patients before surgery, evaluating the effectiveness of interventions, the implications of inaccurate AI recommendations, and how students' acceptance of AI suggestions relates to their accuracy.*
- A randomized trial with medical students employs a factorial design, combining an e-learning module with a quiz and a review of surgical cases to assess HF presence, ensuring systematic selection and reducing potential biases.*
Early Experiences of Integrating an Artificial Intelligence-Based Diagnostic Decision Support System into Radiology Settings: A Qualitative Study.
Stud Health Technol Inform
October 2023
Usher Institute, University of Edinburgh, UK.
Artificial Intelligence (AI) based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based e-learning imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest. We performed semi-structured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings.
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