Alle Papers – KI in der Medizin
153.186 Papers insgesamt · Seite 1 von 400
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
High-performance medicine: the convergence of human and artificial intelligence
Proceedings of the 19th International Joint Conference on Artificial Intelligence
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Systematic review of research on artificial intelligence applications in higher education – where are the educators?
Artificial intelligence in healthcare: past, present and future
ChatGPT for good? On opportunities and challenges of large language models for education
A guide to deep learning in healthcare
Machine Learning in Medicine
The potential for artificial intelligence in healthcare
Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy
Large language models in medicine
2009 International Joint Conference on Artificial Intelligence
Proceedings of the International Joint Conference on Artificial Intelligence 2007
Large language models encode clinical knowledge
The ethics of algorithms: Mapping the debate
Artificial intelligence in healthcare
ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Proceedings of the 16th international joint conference on Artificial Intelligence - IJCAI '99
Scalable and accurate deep learning with electronic health records
ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope