Episode 18: Artificial Intelligence

Author: Eoghan Colgan    @eoghan_colgan
Special Guest: Greg McKelvey   @DrGregMcK



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Guest Bios

Greg Mckelvey

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Dr. T. Greg McKelvey Jr. MD, MPH, is the CMO at KenSci, where he leads teams of doctors and data scientists to apply healthcare machine learning at scale across 3 continents. Prior to his role at KenSci, Dr. Greg served as Medical Director at Epsilon. Dr. Greg trained in Occupational & Environmental Medicine and Biomedical & Health Informatics at the University of Washington (UW).  Dr. Greg received his Masters of Public Health (M.P.H) from the Johns Hopkins Bloomberg School of Public Health and his Doctorate of Medicine (M.D.) from Dartmouth Medical School.

Show Notes

Greg McKelvey is Chief Medical Officer at KenSci, a Seattle-based company exploring and formulating the impact of Machine Learning in predicting disease management risk. Eoghan and Greg discuss the basics of Artificial Intelligence in healthcare - what it means, current applications, ethical issues and predictions for the future.

Take Home Points

What does it mean?

Artificial Intelligence

  1. using technology to replicate intelligence
  2. when a machine mimics "cognitive" functions that humans associate with other human minds, such as "planning", "deciding", "learning" and "problem solving"

Machine Learning

  1. a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed


Artificial Intelligence in daily life

examples include:

  1. search engines such as google
  2. all social media sites
  3. online shopping and advertisements
  4. face recognition software
  5. spam filters
  6. autopilots and GPS units


Artificial Intelligence in Healthcare

  1. current applications include image recognition which is at or above human capability
    • diabetic retinopathy, radiology scans, skin cancers etc
  2. current research in Glasgow:
    • analysing data of COPD patients
    • predicting which patients will require admission
    • predicting how long they will likely stay in hospital
    • using wearables/remote-monitoring to intervene and avoid admissions


Prediction for the future

  1. machines will take over mechanical/repetitive tasks
    • data entry
    • information retrieval
  2. they can handle huge amounts of data to provide additional support:
    • predict patient flow and patient outcomes
    • offer diagnostic support such as image analysis
  3. doctors will do more things that are uniquely human
    • problem-solving
    • creativity
    • patient interaction and communication


An uncertain future

  1. the impact of artificial intelligence on humans is very uncertain
    • reflected in the polarising views held by luminaries in the field
  2. it is unlikely there will be a world without the need for doctors but no-one really knows for sure
  3. some fields of medicine will be more impacted than others depending on how big a fraction of their current role is automatable e.g. radiology


Ethical issues

  1. tech giants have independent regulatory advisory boards
  2. local work (such as the COPD project) is covered by local institutions who protect patient privacy/data etc


Recommendations for those with an interest

  1. we should probably all have some degree of interest in the field as it will likely impact us all
    • be hands-on, sceptical and inquisitive
  2. doctoral-level training is not required and there are fantastic ways to develop without formal training
    • MOOC's and other online courses
      • e.g. https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x-3
      • https://hackr.io/tutorials/learn-artificial-intelligence-ai
    • practice with data sets using open-source machine-learning platforms from google/amazon/microsoft etc
      • (see below)
  3. learn to code - even a basic understanding could be helpful if you intend to be more involved in AI




Google AI



Microsoft AI



Amazon Machine Learning



Big Data and Machine Learning in Healthcare



AI’s Birth, 1956

“We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.” https://www.aaai.org/ojs/index.php/aimagazine/article/download/1904/1802

What Machine Learning Can and Can’t Yet Do



AI & employment



Getting Started in ML




Healthcare Data Sets




Artificial Intelligence vs Stupidity



Do the Amish use Google?






KenSci and GG&C


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