· Frank SR. Digital health care – convergence of health care and the internet. J Ambul Care Manage 2000;23(2):8-17.
· World Health Organization. [New release] WHO release first guideline on digital health interventions. 2019.04.17.
· Shin SY. Current status and future direction of digital health in Korea. Korean J Physiol Pharmacol 2019;5(5):311-315
· Meskό B, Drobni Z, Bényei É, Gergely B, Győrffy Z. Digital health is a cultural transformation of traditional healthcare. mHealth 2017;3:38.
· http://dx.doi.org/10.21037/mhealth.2017.08.07.
· 문세연, 윤영미, 한태화, 이상은, 정혁재, 송시영 등. 디지털 헬스케어 서비스에 대한 보건의료제공자의 인식. 보건정보통계학회지. 2018;43(1):54-63.
· 송영준. 4차 산업혁명과 디지털 헬스케어 정책. 주간기술동향(정보통신기획평가원) 1832호. 2018.
· Mathews SC, McShea MJ, Hanley CL, Ravitz A, Labrique AB, Cohen AB. Digital health: a path to validation. Digital Medicine 2019;2:3. https://doi.org/10.1038/s41746-019-0111-3.
· World Health Organization. [WHO guideline executive summary] recommendations on digital interventions for health system strengthening. 2019.
· U.S. Food & Drug Administration. https://www.fda.gov/medical-devices/digitalhealth.
· Standing M, Hampson E. Digital health in the UK: an industry study for the office of life sciences. Monitor Deloitte. 2015.
· 서울아산병원. 4차 산업혁명시대 미래형 스마트 클리닉 모델 창출. 뉴스매거진 제584호. 2018.7.1.
· 조영희. 삼성SDI 의료 AI 소개 (안저영상분석 중심). 2019 대한의료정보학회 춘계학술대회 심포지엄 1 발제내용: 고려대학교 의과대학 유광사홀. 서울. 201.7.11-12.
· 김수정. 독일 원격의료제도의 과거와 현재. 대한의료법학회 월례학술대회 발제자료. 2020.1.
· 장우애. 아베의 성장 로드맵 <Society 5.0>과 시사점. IBK 경제연구소. 2018.4.
· 서경화. 2020. 디지털 헬스의 최신 글로벌 동향 대한의사협회. 의료정책연구소
· 오지현. (2017). 의료용 인공지능의 허가에 대한 비교제도론적 고찰: 미국•유럽•중국•일본을 중심으로 (Doctoral dissertation, 연세대학교 보건대학원).
· Hwang, E. J., Park, S., Jin, K. N., Im Kim, J., Choi, S. Y., Lee, J. H., ... & Ferretti, G. R. (2019). Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs. JAMA network open, 2(3), e191095-e191095.
· Nam, J. G., Park, S., Hwang, E. J., Lee, J. H., Jin, K. N., Lim, K. Y., ... & Park, C. M. (2018). Development and validation of deep learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology, 290(1), 218-228.
· Myers, E. R., Moorman, P., Gierisch, J. M., Havrilesky, L. J., Grimm, L. J., Ghate, S., ... & Kendrick, A. (2015). Benefits and harms of breast cancer screening: a systematic review. Jama, 314(15), 1615-1634.
· National Health Service(NHS). “benefits and risk breast cancer screening”, 2018/03/27, https://www.nhs.uk/conditions/breast-cancer-screening/
· Jung Hyun Yoon , Eun-Kyung Kim , Byoung Wook Choi , Kyunghwa Han , Hye Mi Gweon , Bomi Kim , Hee Jung Suh(2019) Diagnostic performance of artificial intelligence (AI)-based diagnostic support software for mammography: results using a standardized test set built for external validation. The 75th Korean Congress of Radiology and Annual Delegate Meeting of The Korean Society of Radiolgy. 269.
· Eun-Kyung Kim, Sieun Lee, Hak Hee Kim, Boo-Kyung Han, Eun Hye Lee, HyoEun Kim Increase of cancer detection rate and reduction of false-positive recall in screening mammography using artificial intelligence - a multi-center reader study. The 75th Korean Congress of Radiology and Annual Delegate Meeting of The Korean Society of Radiolgy. 269-270.
· Pages, F., Kirilovsky, A., Mlecnik, B., Asslaber, M., Tosolini, M., Bindea, G., ... & Zatloukal, K. (2009). In situ cytotoxic and memory T cells predict outcome in patients with early-stage colorectal cancer. Journal of clinical oncology, 27(35), 5944-5951.
· Hwang, W. T., Adams, S. F., Tahirovic, E., Hagemann, I. S., & Coukos, G. (2012). Prognostic significance of tumor-infiltrating T cells in ovarian cancer: a metaanalysis. Gynecologic oncology, 124(2), 192-198.
· Dieu-Nosjean, M. C., Antoine, M., Danel, C., Heudes, D., Wislez, M., Poulot, V., ... & Lebecque, S. (2008). Long-term survival for patients with non–small-cell lung cancer with intratumoral lymphoid structures. Journal of Clinical Oncology, 26(27), 4410-4417.
· Open Source Software(OSS). “[주간 OSS 동향 리포트] 中 바이두, 암 진단 속도 높인 AI 알고리즘 발표”, 2018/06/26
· 유효정. “AI가 ‘암’ 발견 앞당긴다… 中 바이두 개발 발표, 딥러닝 알고리즘으로 종양 진단 속도 높여”, ZD Net Korea, 2018/06/20
· Kang, E., Min, J., & Ye, J. C. (2017). A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction. Medical physics, 44(10), e360-e375.
· Nitta, S., Tsutsumi, M., Sakka, S., Endo, T., Hashimoto, K., Hasegawa, M., ... & Nishiyama, H. (2019). Machine learning methods can more efficiently predict prostate cancer compared with prostate-specific antigen density and prostatespecific antigen velocity. Prostate International.
· Park, G. W., Kim, J. Y., Hwang, H., Lee, J. Y., Ahn, Y. H., Lee, H. K., ... & Kim, Y. S. (2016). Integrated GlycoProteome Analyzer (I-GPA) for automated identification and quantitation of site-specific N-glycosylation. Scientific reports, 6, 21175.
· 김윤미. “암 치료 분야의 정밀의학, 어디까지 왔나?”, 메디포뉴스, 2018/05/11
· Kim, J., Lee, I. H., Cho, H. J., Park, C. K., Jung, Y. S., Kim, Y., ... & Seol, H. J. (2015). Spatiotemporal evolution of the primary glioblastoma genome. Cancer cell, 28(3), 318-328.
· 식품의약품안전평가원. ‘신개념 의료기기 전망 분석 보고서’(2017.02)
· 강기헌. "10년 안에 인공지능 수술 로봇이 외과 의사 대신할 것", 중앙일보, 2017.10.12
· Cetin, I. A., DEĞERLI, A. D., Ergelen, R., ÖZGEN, E., & Sevindik, M. (2016). Comparison of Contouring Results for Prostate Cancer Treatment Planning Obtained by Two Different Specialists. TURKISH JOURNAL OF ONCOLOGY, 31(4).
· Davis, B. C., Foskey, M., Rosenman, J., Goyal, L., Chang, S., & Joshi, S. (2005, October). Automatic segmentation of intra-treatment CT images for adaptive radiation therapy of the prostate. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 442-450). Springer, Berlin, Heidelberg.
· Peroni, M., Ciardo, D., Spadea, M. F., Riboldi, M., Comi, S., Alterio, D., ... & Orecchia, R. (2012). Automatic segmentation and online virtualCT in head-andneck adaptive radiation therapy. International Journal of Radiation Oncology* Biology* Physics, 84(3), e427-e433.
· Mathieu, R., Martin, E., Gschwind, R., Makovicka, L., Contassot-Vivier, S., & Bahi, J. (2005). Calculations of dose distributions using a neural network model. Physics in Medicine & Biology, 50(5), 1019.
· Fan, Jiawei, et al. "Automatic treatment planning based on three‐dimensional dose distribution predicted from deep learning technique." Medical physics 46.1 (2019): 370-381.
· Chang ATY, Hung AWM, Cheung FWK, Lee MCH. Comparison of planning quality and efficiency between conventional and knowledge‐based algorithms in nasopharyngeal cancer patients using intensity modulated radiation therapy. Int J Radiat Oncol Biol Phys. 2016;95:981–990.
· Tol JP, Delaney AR, Dahele M, Slotman BJ, Verbakel WF. Evaluation of a knowledge‐based planning solution for head and neck cancer. Int J Radiat Oncol Biol Phys. 2015;91:612–620.
· Zieminski S, Khandekar M, Wang Y. Assessment of multi‐criteria optimization(MCO) for volumetric modulated arc therapy (VMAT) in hippocampal avoidance whole brain radiation therapy (HA‐WBRT). J Appl Clin Med Phys. 2018;19:184–190.
· 인터넷마케팅. “코난테크놀로지, 고려대 안암병원과 함께 `암 환자 방사선 치료 인공지능 활용 연구` 발표”, 디지털타임스, 2019/05/13
· 비즈포아이알. “빅데이터로 방사선 치료의 부작용 예측”, 포아이알뉴스, 2017/11/08
· Kim, K. H., Lee, S., Shim, J. B., Chang, K. H., Cao, Y., Choi, S. W., ... & Kim, C. Y. (2017). Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study. Journal of Radiotherapy in Practice, 16(2), 161-170.
· 과학기술정보통신부. “인공지능(AI)+빅데이터 활용 고속 신약개발 플랫폼, 19년 출시한다.”, 2018/02/05,
· 배영우. (2017). 인공지능을 이용한 신약개발 동향 및 사례, Vol.44호
· 남도영. “내년에 AI+빅데이터 신약개발 플랫폼… 비용•기간 확 줄어든다”, 디지털타임스, 2018/02/02
· 김지섭. “신약개발에 인공지능 적용 … 시간•비용↓ 성공확률↑”, 디지털타임스, 2017/09/21
· 강승지. “’인공지능(AI) 신약개발지원센터’ 공식 오픈”, 히트뉴스, 2019/03/20
· 허영. (2016). Big data of medical imaging for customized smart healthcare, KEIT PD Issue Report Vol. 16-03
· 국가과학기술지식정보서비스(NTIS), “대형병원 Case Study 판독사례의 검색을 포함하는 Cloud 기반의 웹 의료영상저장전송시스템(PACS) 플랫폼 개발”,
· https://www.ntis.go.kr/sims/pjtinfo/pjtMainInfo.do?pjtInfoVo.pjtId=1415155531
· 정규환. (2018) 인공지능 기반 의료영상 분석 기술 동향, 정보통신기획평가원
· 박태신. (2014). 보건의료정보 공개와 개인정보 보호. HIRA 정책동향, 8(6), 33-42.
· 이지혜, 제미경, 조명지, & 손현석. (2014). 보건의료 분야의 빅데이터 활용 동향. 한국통신학회지(정보와통신), 32(1), 63-75.
· Chang, H. Y., Jung, C. K., Woo, J. I., Lee, S., Cho, J., Kim, S. W., & Kwak, T. Y. (2019). Artificial intelligence in pathology. Journal of pathology and translational medicine, 53(1), 1.