The book 'There's no such thing as K-Quarantine', written by Prof.Howard Lee, Jung-Hyun Won, YeSol Hong and 13 others, was selected in the liberal arts section of the Sejong Cultural Book Program this year. The Sejong Cultural Book Program is organized by the Publication industry Promotion Agency of Korea. This program aims to promote domestic publishing activities and to build a knowledge-based society by selecting outstanding books among those published throughout the year. Books worth 8 million Korean won will be distributed to public libraries nationwide. In 2022, 550 books were selected from a total of 8,698 candidates. The book There's no such thing as K-quarantine was selected as an excellent book in the field of technology science. 

Two scientists from Samsung SDS, Mr. Jaeho Yang, Principal Engineer (Sr. Expert), IoT Lab, and Dr. Sunghoon Joo, PhD, Senior Engineer, Advance Research Lab, visited CCADD on August 24, 2018 to have a joint seminar. In this joint seminar, Prof. Howard Lee gave an overview of the research projects CCADD has performed in the area of applying the artificial intelligence (AI) technology to clinical drug development, entitled with 'AI-enriched Clinical Trials'. Prof. Lee emphasized how AI could streamline various research activities pertaining to clinical trials, one of which is patient enrollment. For example, it is important to know who did what and how before planning any clinical trial, which is the main theme of the grant that the Korea Clinical Trials Global Initiative or KCGI has recently awarded CCADD, named 'AI-based Clinical Trial Resource Integration System or ACTRiS',

Following Prof. Lee's presentation, Mr. Yang introduced CCADD to the core concepts of several AI technologies and their potentials in drug discovery. Recurrent Neural Network (RNN) and autoencoding (AE) were a few examples Mr. Yang put emphasis on during his presentation. After that, Dr. Joo expanded further on several research projects Samsung SDS has conducted to design new molecules for drug discover in silico using advanced AI technologies such as molecular graphs and AE. Collectively, Mr. Yang and Dr. Joo showed how AI could fasten the discovery of new drug candidates that are likely to maintain excellent bio-physico-chemical properties, which may save time, resources, and costs in the whole drug development program. 

CCADD and Samsung SDS agreed to continue looking for a cadre of collaboration and joint research opportunities that may benefit both organizations.   

Dr. Yuchae Jung introduced the concept of P-MATCH or Precision Machine Learning AssisTed Clinical Trial Eligibility Assessment using Hospital Records at the 5^th Joint Conference on Medical Device between Seoul National University Colleges of Medicine and Engineering and Seoul National University Hospital on November 3, 2017. The title of her talk was 'Application of Artificial Intelligence Technology for Efficient Clinical Trials'. Dr. Jung underlined why artificial intelligence, mainly deep machine learning, can and should be adopted to bring more efficiency to clinical trials, particularly for precise patient enrollment, which allows for patients who are likely to respond to a treatment under testing to have a bigger chance of enrollment. This will also make clinical trials more productive and error-free, Dr. Jung added, because all of the eligibility assessment procedures are algorithm-based using the electronic medical record (EMR). 

Dr. Jung's talk was well received, and there were a couple of questions as to what could make P-MATCH different from other approaches such as the IBM Watson for Oncology. Dr. Jung replied that the biggest difference is P-MATCH's natural language processing capability of more than one language (i.e., Korean and English), which often are used in EMR in a haphazard and inconsistent manner. 

For anyone who wants to learn more about Dr. Jung's presentation, please contact her. 

The Convergence Story, a periodical news letter published by the Graduate School of Convergence and Science Technology (GCST) at Seoul National University, interviewed Dr. Yuchae Jung on Sep 21 2017 about her past research experience and accomplishments, new research interests, and future plans. In the interview, Dr. Jung emphasized the application of artificial intelligence technology to streamline clinical drug development, particularly for efficient patient enrollment using machine learning algorithms based on the electronic medical record (EMR). More details about Dr. Jung's interview can be found at News letter.  

How can we increase the efficiency, accuracy, and speed of patient enrollment into clinical trials? Clinical trial investigators have long been suffering from the low yield, inaccuracy, and delay of patient enrollment. It was mainly because the current practice is labor-intensive and has to be done manually in most cases. For example, based on the eligibility criteria of a clinical trial, a clinical trial investigator or one of his/her staff (e.g., clinical research coordinator) has to go over the entire medical record of a potential patient by eyeballing many scattered fields, which is very much time-consuming while inefficient and inaccurate. A typical clinical trial protocol has an average number of 50 for the eligibility criteria, which can become easily many more up to ~100 in late phase clinical trials. Dr. Lee discussed this issue and how artificial intelligence-guideded deep learning could address it at a recent Bio-Nanomedicine Salon (August 31, 2017) co-sponsored by the Korean Society for Nanomedicine and the Seoul National University Bio-Max Institute. 

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