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Artificial Intelligence in Medicine Wei-Hung Weng, MD, MMSc UP-MIT-Stanford-AeHIN Big Data for Health Conference and Workshops for Asia-Pacific July 4, 2017

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Artificial Intelligence in Medicine

Wei-Hung Weng, MD, MMSc UP-MIT-Stanford-AeHIN Big Data for Health Conference and Workshops for Asia-Pacific July 4, 2017

Disclosure

•  No conflict of interest

Gulshan, JAMA 2016

Medical Imaging

Esteva, Nature 2016

Medical Imaging

De Choudhury, HCI 2016

Medical Text

https://cbi-blog.s3.amazonaws.com/blog/wp-content/uploads/2017/01/

healthcare_AI_map_2016_1.png

Startups

https://mimic.physionet.org/ http://eicu-crd.mit.edu/

Multimodal Data

•  2008-2013 map

Courtesy by Owen Hsu (Wistron)

Adoption of EHR

http://doccartoon.blogspot.sg/2011/09/7-types-of-physician-bad-handwriting.html

No More

https://www.linkedin.com/pulse/most-bizarre-icd-10-codes-infographic-nina-keller

Standardization

Democratization of Knowledge and Resources

Clinical Perspective •  Cost / Risk assessment and adjustment –  Insurance – Resource reallocation

•  Precision / Personalized medicine – Oncology / rare diseases / mental disorders / … – Applications

•  Clinical decision support •  Drug discovery •  Outcome prediction

–  Lifespan prediction / Disease progression •  Chronic disease management

–  Early prediction of blood glucose for self-management

CS/AI/ML Perspective

•  Risk stratification •  Causal inference •  Bias •  Time-series •  Unstructured data •  Interpretability •  Disease progression modeling •  Reasoning and decision making

http://www.telegraph.co.uk/news/2017/03/17/security-breach-fears-26-million-nhs-patients/

Data Security

https://xkcd.com/1838/

Transparency

http://www.businessinsider.com/the-worlds-first-artificially-intelligent-lawyer-gets-hired-2016-5

Replacing Your Job?

Limitations of AI •  Autonomous manipulation in unstructured

environment •  Emotion •  Thinking, reasoning and decision making –  Inference –  Abstraction –  Cognition –  Commonsense knowledge –  Insight

•  Creativity •  Social intelligence

http://knowyourmeme.com/photos/234739-i-have-no-idea-what-i-m-doing

Osborne, MILA 2017

Jha, JAMA 2016

What We Can Do •  Find good problems, collect reliable data > Big data

and algorithms •  Collaboration > Expert or AI only –  AI: pattern recognition and massive repetitive tasks –  Expert: high-level integration, interpretation and

decision making •  Sharing –  Engineers: open-source platforms and algorithms –  Clinicians: open-source data and knowledge

•  Experience –  Learning how AI works –  Communicating with machine (programming)

Courtesy by Dr. Andrew Beck (PathAI) Wang, arXiv 2016

Take Home Message •  Data-driven, ML-based approach, with standardized medical

language •  Defining good problems + finding reliable data sources

–  Cost, risk, precision medicine –  Causality, bias, unstructured data, interpretability, reasoning and

decision making •  Collaborating with machine and human community (sharing) •  Experiencing, learning and communicating

•  Acknowledgement –  Leo Celi (MIT), Alvin Marcelo (UP-AeHIN), Mornin Feng (NUS) –  Peter Szolovits (MIT)

•  [email protected] / Wei-Hung Weng (LinkedIn) •  http://ckbjimmy.github.io/2017_cebu