AI + Medicine

Video

Agenda

Real word AI application in Medicine

Lots of data in healthcare

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Problems

How AI will transform Health Professional

Clinical CategoryData Interpretation
RadiologistScans
PathologistsSlides
DermatologistsSkin Lesions.
OphthalmologistsEye exams
CardiologistsECG, Echo
OncologistsOmics, Rx
GeneticistsFace, BAM file
Palliative CarePrediction
All DoctorsDelete Keyboards
NursesVital signs
PharmacistsDrugs
PsychiatristPsych status
GastroenterologistsScope

AI across the health span

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Example of applications:

Using hundreds and thousands million examples that human expert can’t

Random trials of AI DNN

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The virtual medical assistant

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The bottleneck problems.

Conclusion

Machines will not replace physicians but physician using AI will soon replace those not using it.

Panel

Challenges working in an intersection between AI and Healthcare:

Digital process of healthcare:

How does Stanford Healthcare demonstrate AI application?

The use of AI for Chest X-ray. Problem: logistical in working data, regulation, quality control, IT infrastructure to answer basic research question. Control setting and real world data setting, e.g. between different countries. Time of execution, training time. A robust model for clinical physicians to work in real world

How meaningful is a bridge between AI/ tech and medicine?

Different in science culture and disclosure. Example: publication process between two communities, e.g. pre-print and public discussion and then peer-review.

Motivation to close the gap, a willingness to improve conversation.

Why someone would jump into healthcare?

More data is digital. Lots of room in healthcare system, e.g. software and human. There is more eagerness to exchange knowledge between communities.

How AI can help healthcare human again? (We thought the in a converse way)

The key point most of us miss is a degradation of human element in healthcare and medicine. Since 80s, less and less between doctors and patients because of time constraint, shift work and clinical work which eventually lead to a global burn out and depression in doctors and nurses. Increase accuracy, speed and efficiency. The gift of time help us trust and bond between patients and the system.

How to make life and death decision supported by black-box AI? How explainable is helpful?

Similar to the process of two physicians communicating with each other to make decisions. How algorithms work, how data is mapped, lab test there is a rational decision behind the scene.

Doctors need explanation not for day-to-day decision making but faith for something reasonable enough. There is a bag of techniques for decision-making

What are the advantages of AI during this pandemic?

Detection and hospital management. We are in a half year not a year and half. Situation is unpredictable. A smart thermometer could be a way to localize an high potential area of the outbreak, then people can get in, test, isolate, contact tracing – the best prevention practices. Another example is Fitbbit and Apple smart watches to capture data like a resting heart rate. Other ways like digital surveillance, keep people out of the hospital by staying at home with devices but the problem here is that we don’t have proof about its safety and algorithm.

How to stay updated with the community?

Why aren’t these system widely deplyed in hospitals yet?

Practical bottleneck for ML:

Human vs machine learning: top 10 conditions problems

Given an image and a medical history, a radiologist can diagnose any of a large number of conditions.

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Change management

ML model vs ML system

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Tagged #ai, #summary.