Alle Papers – KI in der Medizin
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The false hope of current approaches to explainable artificial intelligence in health care
<p>Taiwan’s National Health Insurance Research Database: past and future</p>
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2017)
Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers
The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care
DARPA's Explainable Artificial Intelligence Program
Artificial intelligence in higher education: the state of the field
ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations
Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations
Artificial Intelligence in Cardiology
The Medical Segmentation Decathlon
Artificial Intelligence in Dentistry: Chances and Challenges
Experimental evidence on the productivity effects of generative artificial intelligence
Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration
A SWOT analysis of ChatGPT: Implications for educational practice and research
Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence
The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI
The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database
Explainable AI: from black box to glass box
Chatting about ChatGPT: how may AI and GPT impact academia and libraries?
Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios
Ensuring Fairness in Machine Learning to Advance Health Equity