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2024_개보위_개인정보 미래포럼_의료 인공지능 모델과 프라이버시 이슈.pdf
Medical AI needs more datasets to improve accuracy and help clinicians, but collecting and labeling medical data poses privacy concerns. Researchers are exploring self-supervised and generative AI techniques to address these challenges by generating synthetic medical data without privacy issues. As language models and foundation models continue to grow in scale and capability, they may help advance medical AI by generating diagnostic reports, summarizing studies, and aiding discovery through analysis of vast amounts of data. However, privacy and bias remain important issues to address with further development of these techniques.
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2024_개보위_개인정보 미래포럼_의료 인공지능 모델과 프라이버시 이슈.pdf
- 1. Why does medicalAI need more datasets? And Its privacy concerns. 의료 인공지능 모델과 프라이버시 이슈 Medical Imaging & Intelligent Reality Lab. Convergence Medicine/Radiology, University of Ulsan College of Medicine Asan Medical Center Namkug Kim, PhD www.mi2rl.co
- 2. Conflict of Interests Establishmentsof Somansa Inc, CyberMed Inc, Clinical Imaging Solution Inc, and Anymedi Inc. Researches with LG Electronics, Coreline Soft Inc.,, Osstem Implant, CGBio, VUNO, Kakaobrain, AnyMedi Inc.
- 3. Venn Diagram ofGenerative AI AI ML(1950s-) DL(2012-) Generative AI Computer Vision (image, video) NLP (Text, speech) LLM Data Science Big Data LVM LMM Modified by NKim
- 4. Challenges in Medicine? 4 Clinical Needs More accuracy, Help for clinician, Cost effective, Workflow optimization Etc. Technical Push Digital Transform, AI, Deep Learning, LLM, Genetic Evolution, Robotics, etc AI(deep learning) Minimal invasive surgery Robotics Digital Transform ChatGPT (LLM) Computer Aided Surgery Generative AI On device AI, Edge computing, (On premise) LVM/LMM Foundation Model Digital Twin Artificial Organ AGI Causal AI Quantum AI Intelligent / Quantitative Medicine Learn2Learn Autoregressive Continual AI Metaverse LangChain Predictive Medicine Quantum computing
- 5. Five tribes ofAI research From Pedro Domingos, Professor, University of Washington at ML conf ATL
- 6. Connectionism Comparative psychologist S-R frameworkof behavioral psychology Associations forming between stimuli and responses. become strengthened or weakened by the S-R pairings. trial and error learning due to rewards. connectionism : learning could be adequately explained without referring to any unobservable internal states. 6 Ivan P. Pavlov(1849-1936), Nobel Prize@1904 Edward Lee Thorndike (1874-1949) Oliver Gordon Selfridge (1926-2008) Thorndike’s “Law of Effect” (1920’s) Reward strengthens connections for operant response Selfridge’s Pandemonium (1955) A parallel, distributed model of pattern recognition Lindsey & Norman’s ‘Human Information Processing’ (1977)
- 7. Comparison btw Brainand NN 7
- 8. Representation Learning Encoder, Decoder,Latent Space 9
- 9. Data Labeling Number1 Acceptable Performance 55000labels per class Human Level Performance 1M labels per class Issues especially in medical data Too expensive Inaccurate Incomplete/ambigous laborious 11 Ian Goodfellow, Yoshua Bengio, Aaron Courville 'Deep Learning’ https://www.deeplearningbook.org/ How infants may learn …
- 10. Baby/Animal perception 12 Courtesy ofLe Cun’s presentation. Courtesy of Le Cun’s presentation. Hubel and Wiesel, The Journal of physiology 148(3), 574–591 (1959)
- 11.
- 12. Big Transfer (BiT):General Visual Representation Learning (2020) 14
- 13. Self-Supervised Learning: ContrastiveLearning Pretext Tasks Learn representation that could help downstream taks Exemplar (2014), Context Prediction (2015), Jigsaw puzzle (2016), … Contrastive Learning Make similar/dissimilar data near/far 15 https://blog.research.google/2021/06/extending-contrastive-learning- to.html BERT (Devlin 2018), RoBERTa (Ott 2019) Courtesy of Alfred Canziani
- 14. ML vs SSL 16 데이터~ 가우시안 분포
- 15. Self-Supervised Learning: ContrastiveLearning 17 Emerging properties in self-supervised vision transformers. ICCV 2021 (pp. 9650-9660).
- 16.
- 17. Generative Model 20 Courtesy ofSeungbin Im, https://horizon.kias.re.kr/25133/
- 18. How to teach: Supervised, Semi-Supervised, Unsupervised, Self-Supervised 23 https://arxiv.org/pdf/2002.08721.pdf
- 19. Autoregressive Inference 28 Obligatory RNNdiagram. Source: Chris Olah.
- 20. 1542M 762M 345M 117M GPT-1 2018, Onewayattention w transformer GPT-2 2019, 1.5B parms on 40G texts (10 times bigger) GPT-3: 2020,175B parms on 570GB? texts (100 times bigger) GPT-3.5: 2022.11(5), 175?B parms GPT-4: 2023.3(8), 1.74?TB parms (4K -> 8K -> 32K -> 128K tokens) PaLM： 540B parms PaLM2： ?Ｂ parms Claude : ? parms, (100K token), w custom transformer Gemini : 2023.11, ?T parms (32k token) w PaLM Gemini Ultra : 2024.2 LLM : Size matter! https://jalammar.github.io/illustrated-gpt2/
- 21. Emergent Ability ofLLM Emergent ability: Non-Linearity of LLM’s performance at specific size! Interactive Zero-shot, few-shot learning Etc Prompting, Chang-of- thought 32 arXiv:2206.07682; Google Research, Stanford, UNC Chapel Hill, DeepMind
- 22. Emergent Ability ofLLM 33 Wei et al., 2022; also see Nye et al., 2021
- 23. Scaling Law inLLM LLM 성능 (데이터 크기 D, 계산량 C, 모델크기(w/o 임베딩) N) • 모델의 형태(width, depth) << 모델의 크기 • 다른 항목에 의해 병목 현상이 발생하지 않는 경우 세 가 지 요소 N, D, C과 각각 power-law관계 • N와 D를 같이 키우면 성능은 예측 가능하게 증가 , 한쪽을 고정하면 어느 시점에서 성능이 향상되지 않는다. → 큰 모델은 더 많은 데이터를 필요로 한다. • 모델 크기(w 임베딩 레이어), 모델 크기 증가 != 성능향상, 모델 크기(w/o 임베딩 레이어), 모델 크기 증가 -> 성능향 상 → 임베딩 레이어 크기가 매우 큼. • 큰 모델은 작은 모델에 비해 동일한 성능에 더 적은 데이 터로 도달. → 데이터 대비 학습 효과가 좋다(Test Loss 기준). 34 arXiv:2001.08361, Jared Kaplan ∗,. Open AI -> Anthropic
- 24. Data scale forLLM 35 https://github.com/Mooler0410/LLMsPracticalGuide
- 25. RLHF: RL withHuman Feedback Training language models to follow instructions with human feedback, arXiv:2203.02155
- 26. Unsupervised, Supervised fine-tuning,RLHF The development of an LLM visualised as the beast Shoggoth (courtesy of Helen Toner’s post)
- 27. 4 months: EmergingTopics; LLM
- 28. Evolutionary Tree ofLLM; 선캄브리아 진화 폭발적, 지수적 2017 transformer 2020 거대 모델 emergent ability 2022 거대모델 대중화 2023 5-7 3개월 만여개 LLM 10, 100, 10000 10 foundation model 100 application models (2주) 10000 finetuning models (의지) 43 # Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
- 29. 랭체인(LangChain) 언어 모델(LLM)을 기반으로애플리케이션을 구축하기 위한 오픈 소스 프레임워크 54
- 30. LLM extension AlphaZero(2018) ->AlphaTensor(2022), AlphaDev(2023) w transformer AlphaCode2(2023) w Gemini Pro, AlphaMissense(2023), GNOME(2023) AlphaGeometry(2024) FunSearch (PaLM2) Making new discoveries in mathematical sciences using LLMs 55 Nature 625, 468–475 (2024).
- 31. Foundation Model 56 “AI isundergoing paradigm shift with the rise of models that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.” -> foundation models • Keys for foundation models • Unsupervised/Self Supervised Learning • No lables • Easy Adaptation • Zero-shot: apply to new tasks without any training examples for those specific tasks • Linear probe: train a linear model on the features • Fine-tune: adjust the entire network to perform better in the target task • Examples: BERT, GPT, CLIP, etc Rishi Bommasani, arXiv:2108.07258, Standford, 200pg Opportunities Increase developer velocity Reduce infrastructure cost Tapping into state-of-the-art AI Risks Bias propagation Increasing model scale Closed-source mode
- 32. 57 • Long tail? • Explainable and generalizable model • Privacy preserving • Integraiton with LLM
- 33. Dataset: 904,170 CFPs fromMEH-MIDAS, Kaggle EyePACS 736,442 OCT scans from Cell 172, 1122–1131.e9 (2018) Processing: AutoMorph, 256x256, Masked autoencoder with encoder, ViT-large; decoder, ViT-small With SimCLR, SwAV, DINO, MoCo-v3 Eight NVIDIA Tesla A100 (40 GB), 14day 58 Nature, 2023
- 34. My Recent Research GenerativeModel Diffusion Inversion/AE LVM, LMM Foundation model Digital Twin AR/3DP/Robot Integration 63
- 35. 혁신 경쟁 어디서오는가? 중국 vs 미국/유럽 미국 vs 유럽 한국은? 83 VS VS
- 36. Big data :How to create value IoT Thermometer Kinsa : Startup @ USA Real-time body temperature bigdata@USA Patients-derived health data Regional basis Influenza stats Kinsa : Realtime vs CDC : 3 week delay B2B model: Demands and production 독감 예방 접종 혹은 항생제, 살균제 등의 약 칫솔, 오렌지 쥬스, 수프 등 Influenza trends: comparison with CDC 2.5 y
- 37. Innovation of Openness Since1980 for big data research 85
- 38. IoT + AI? Dexcom 연속혈당측정기기:1형당뇨 Proteus Digital Health 알약형 센서: 환자의 약 섭취 여부 기록 C형 간염 환자 : Gilead의 Harvoni (수만 UDS/1m) 애플워치 (wearable device) 불면증, 고혈암, 심장 부정맥, (암, AD) Color Genomics, 23andMe 유전자 키트 86 소아당뇨(TypeI) 환자가 한달동안 맞아야 하는 인슐린 주사양. 추가로 수시로 혈당을 체크하기 위해 바늘을 하루 최대 20번 찔러야 한다. (대니재단제공)
- 39. 개인정보 수집, 이용,생성, 활용 PHR Sharing Network 87 THEDATAMAP https://thedatamap.org/map2013/index.php
- 40. 2019년 GPT-2는 10까지안정적으로 셀 수 없었습니다. 불 과 4년이 지난 지금, 딥러닝 시스템은 소프트웨어를 작성 하고, 필요에 따라 사실적인 장면을 생성하고, 지적 주제 에 대해 조언하고, 언어와 이미지 처리를 결합하여 로봇 을 조종할 수 있습니다. AI 개발자가 이러한 시스템을 확 장함에 따라 명시적인 프로그래밍 없이도 예기치 않은 능 력과 행동이 자연스럽게 나타납니다. AI의 발전은 많은 사 람들에게 놀라울 정도로 빠르게 이루어지고 있습니다. 88
- 41. 가명처리방법 재식별 가능? 법적이슈; 법적 책임 주제 (재식별 당사자) 기술적 이슈; Endless war! 해커와 보안, 자물쇠 와 열쇠 Hash 인정 안됨 -> 인정됨 (Quantum computing?) 89 보건의료데이터가이드라인. 2024.1.19
- 42. 얼굴 아닌 식별력있는 개인정보? 90 Diffusion Model로 생성된 국과수 지문@MI2RL Retina Image GAN https://arxiv.org/pdf/1907.12296 ECG GAN https://arxiv.org/abs/1909.09150
- 43. 재식별 in detail(영상의 경우) DICOM Header 삭제 3D영상의 피부 부분 삭제 (얼굴 vs whole body) 성리학적 세계관에 근거한 근 본주의적 사고 의료영상만 문제? 그 자체가 biometric Fingerprint, retinal pattern Master table이 없는 경우 재 식별화가 안됨 91 정책적 해결 필요 재식별 주체를 처벌! Honest broker 활용 Outlier 케이스 경우 opt in/out으로 처리 Blue button; Data account; opt out / in 선한 사마리안 연구자개념 (vs 회사 연구자와 구분) Sampling 기반으로 평가/처 리 필요
- 44. 생성형 모델에서 개인정보이슈 92
- 45.
- 46. Ethical use ofAI in radiology promote wellbeing, minimize harm, and ensure that the benefits and harms are distributed among the possible stakeholders in a just manner appropriately transparent and highly dependable, curtail bias in decision making, and ensure that responsibility and accountability remains with human designers or operators. The radiology community should start now to develop codes of ethics and practice for AI. Radiologists will remain ultimately responsible for patient care and will need to acquire new skills to do their best for patients in the new AI ecosystem 94 규제 vs 활용의 밸런스
- 47. Collaborators Radiology Joon Beom Seo,SangMin LeeA,B Dong Hyun Yang, Ji Eun Park, Jeong Hyun Lee, Gilsun Hong, Haejeon Hwang, Yangshin Choi Pathology Hyunjeong Go, Gyuheon Choi, Gyungyub Gong, Yongmi Cho, Seongmo Hong, Sangjeong Ahn Orthopedics Kyeonghwan Ko Anesthesiology Sung-Hoon Kim, Eun Ho Lee Neurology Dong-Wha Kang, Jaehong Lee, Beomjun Kim, Eun-Jae Lee, Surgery Beom Seok Ko, JongHun Jeong Songchuk Kim, Tae-Yon Sung, Gastroenterology Jeongsik Byeon, Kang Mo Kim, Do-hoon Kim, Jiyoung Lee, Emergency Medicine Dong-Woo Seo Pulmonology and Critical Care Medicine Sei Won Lee, Jin-won Huh Plastic Surgery Woosik Jeong, Jongwoo Choi Ophthalmology Yoonjun Kim ENT Jongwoo Jeong