Artificial Intelligence Approaches for Medical Diagnostics in Korea
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 91749
Special Issue Editor
Interests: aging science; applied artificial intelligence; digital healthcare; human computer interaction; software engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Korean government has established a plan to make it the world’s third-largest digital competitor and create an economic effect of about 400 billion dollars through AI-based businesses by 2030. In particular, in the healthcare sector, the government announced that it would use AI to foster the biotechnology and medical industries as next-generation core growth industries. With the country’s successful ICT-based response to COVID-19, more attention has also been focused on AI in medicine and its related industry. Due to this, the contribution to AI research in medicine in Korea is rapidly increasing. It seems meaningful at this point to understand and share South Korean research on artificial intelligence approaches for medical diagnostics, so that one could learn its progress, compare it with that of other countries, and anticipate future directions. This Special Issue aims to bring together scholars, professors, researchers, engineers, and administrators in Korea using state-of-the-art technologies and ideas to significantly improve the field of AI and machine learning in medical diagnostic technology, including but not limited to the following:Diagnostics with smart IoT and mobile/wearable devices;
Intelligent in vitro diagnostic analysis;
Medical image analysis and diagnostics;
Text mining and natural language processing in medicine;
Intelligent voice transcription;
Knowledge engineering approaches in medical diagnostics;
Data analytics and mining for biomedical decision support;
AI with electronic medical records;
Artificial neural networks and deep learning in medicine;
Models and systems for AI-based public health.
Prof. Dr. Hee-Cheol Kim
Guest Editor
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Keywords
- Artificial intelligence
- Biosensors and biochips
- Clinical medicine
- Diagnostics
- Knowledge Engineering
- Diagnostic robotics
- Machine learning
- Precision medicine
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