ai and machine learning in healthcare
TRANSCRIPT
MTREC Listen@LunchAI and Machine Learning in
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AI and Machine
Learning in
Healthcare
Nov 2020
Dr. Scott Moen
Outline
What is artificial
intelligence?
What is machine learning?
What is deep learning?
General AI and meta AI
AI in medical coding
AI in clinical research
AI in diagnostics
AI in clinical infrastructure
Implementation details
What is
artificial
intelligence?
A computer program
that is perceived as
human intelligence.
AI ML DL
What is
machine
learning?
A computer program that
derives patterns from data,
instead of relying on explicit
instructions.
AI ML DL
How does ML differ from standardprogramming?
y=mx+bMachine LearningTraditional Programming
Input data
Programmerdefinedprocess
Result
M =2
X = 3
B = 4
def line(M,X,B):
y= M*X+B
return y
Y=10
Training data Input Data
M =2
X = 3
B = 4M =2
X =5
B = 2
???
Fit Model
Training
Results
M =1
X =1
B =4
Y=10
M =2
X = 3
B = 4
Run Model
Inference
Result Y=10
Y = model
.predict(M,X,B)
Y=20
Y=5
Some of the important types of ML and common applications
Unsupervised Learning – the training data has no labels• K-means clustering - an app that groups people based on
personality preferences (music, food choices, etc.}
• Kernel Density Estimation – an app that uses fishing vesselhauls and GPS data to predict the locations of schools offish.
Some of the important types of ML and common applications
Semi-supervised Learning – some of the training data has labels
▪ An app that predicts crop infestation based on pictures of the damage. The algorithm will cluster the pictures based on feature similarity and then name it given the available labels in that cluster.
Some of the important types of ML and common applications
Supervised Learning – the training data is labeled• Classification – an app that predicts the type of fish from a
photo and gives a percent for each category.
• Regression - an app that predicts a house price based on existing data.
Some of the important types of ML and common applications
Reinforcement learning – the algorithm is rewarded/punished for actions
▪ A program plays Pac-Man and is given 1 pt. for every dot eaten and loses 5 pts for each time a ghost kills it. Additionally, the program is taught how to move up/down/left/right. It iterates over and over until a high score threshold is reached.
ML strengths/weaknesses
• User doesn’t have to explicitly define pattern. Especially useful when the data is immense.
• User doesn’t need to know all the possible outcomes.
• The algorithm doesn’t have human biases.
ML strengths
• Garbage in-garbage out
• No common sense
• Algorithm only good for one purpose
ML weaknesses
What is deep
learning?
Deep learning uses networks
that vaguely resemble human
neurons.DOI: 10.1007/978-3-030-00937-3_7
AI ML DL
What differs from standard programming?
from keras.models import Sequential
from keras.layers import Dense
#create model
model = Sequential()
#get number of columns in training data
n_cols = train_X.shape[1]
#add model layers
model.add(Dense(4, activation='relu’,
input_shape=(n_cols,)))
model.add(Dense(5, activation='relu'))
model.add(Dense(1))
Input Layer Hidden Layer Output Layer
Supervised – the training data is labeled
▪ Multilayer Perceptrons – great for basic classification and regression problems; excel sheet type of problems.
▪ Convolutional Neural Networks – the modern version of the perceptron, can handle very complicated data like self-driving cars.
Some of the important types of DL and common applications
Unsupervised – the training data has no labels, learns by clustering
• Autoencoder – great for reducing very complex systems, like an app that looks at all the reviews of a specific movie and finds specific characteristics that certain people like or don’t like.
Some of the important types of DL and common applications
General AI and meta AI
General AI
▪ An AI capable of handling multiple types of data.
▪ GPT-3
▪ IBM Watson
General AI and meta AI
Meta-learning AI
▪ Combines multiple types of AI to get best answer or can be used to optimize hyperparameters of other machine learning models.
3 General Applications of AI
Computer Vision
▪ Algorithms that automate human visual tasks by finding patterns in static images and videos.
▪ Object tracking
▪ Segmentation
▪ Object Detection
▪ Image classification
3 General Applications of AI
Natural Language Processing
▪ Algorithms that process and analyze large pools of human language.
▪ Lexical analysis
▪ Interpret semantics
▪ Pragmatics analysis
3 General Applications of AI
Speech Recognition
▪ Algorithms that interpret and produce sounds.
▪ Voice recognition
▪ Speech-to-text
AI in Diagnostics
A meta-analysis (n=69) published by the Lancet in 2019, showed that health professionals and AI algorithms show a similar sensitivity and specificity for medical diagnostics. https://doi.org/10.1016/S2589-7500(19)30123-2
AI in Diagnostics
Radiology - AI can excel at recognizing patterns in medical images
• Radiology: Artificial Intelligence
AI in Diagnostics
Chatbot - Using natural language processing (NLP), chatbots can acquire patient’s symptoms, triage, and perform a partial diagnosis to aid the healthcare worker.
• Microsoft Healthcare Bot
• Mediktor
AI in Diagnostics
Pathology - Like Radiology, but at cellular level, often incorporating more features such as fluorescent cell markers.
• PathAI
• Paige.AI
AI in Medical Coding
The new ICD-11 has 4x the number of codes compared to the ICD-10 and currently, 51% of human entered codes are inaccurate. AI systems aim to increase both speed and accuracy of the coding procedure.doi: 10.1097/SLA.0000000000000851.
AI in Medical Coding
Interpret multiple data types
• Diagnoss can assess medical images in addition to classifying text.
AI in Medical Coding
On-demand and scalable
• Fathom can regulate itself depending on the workflow, thereby reducing costs.
AI in Medical Coding
Real-Time reporting with dashboards
• 3M™ 360 Encompass™ System has dashboards that produce real-time data for reporting purposes.
AI in Clinical Research
In 2000-2015, just 13.8% of clinical trials passed all three stages of FDA testing. AI aims to speed up the process and increase the success rate.doi: 10.1038/d41586-019-02871-3
AI in Clinical Research
Proper subject attainment• Deep 6 AI uses NLP to process medical records
and reports to find potential subjects in a matter of minutes.
AI in Clinical Research
Direct subject observation
• AICure uses AI in mobile devices to directly monitor patient adherence.
AI in Clinical Research
Design better trials
• trails.ai uses NLP to search previous clinical trials, pertinent regulatory information, and medical journals to design clinical trials.
AI in Clinical Infrastructure
AI can also help with basic operations infrastructure tasks needed by clinical entities.
AI in Clinical Infrastructure
Supply management
• Signant Health- GxP compliant software that digitizes supplies, forecasts quantities, orders supplies, and handles recalls/expired products.
AI in Clinical Infrastructure
Identify patient flow choke points
• Qventus -in real-time can monitor patient flow within a clinic and determine bottlenecks. Additionally, management can create process focus points.
AI in Clinical Infrastructure
Claims and Denials management
• Olive - submits claims faster and more accurately than human, allowing users to concentrate on border cases.
Implementation – Logistical, ethical, and legal considerations to implementing AI.
Implementation - Personnel
Implementation - Cloud vs local inference
Cloud learning
▪ Very powerful and scalable (necessary for onlinemodel)
▪ Can be very expensive▪ 4.6 million to train GPT-3.
▪ End-user must be connected to internet
▪ Possible security concerns
Implementation - Cloud vs local inference
Edge Device
▪ Not very powerful, huge power draw▪ GPT-3 would require 355 years.
▪ Not expensive at all
▪ No internet needed
▪ Cannot create model, only run inference
Implementation – Legal Concerns
HIPAA - One of the most central concerns to multiple facets of integration.
• HIPAA data necessary for developing most medical AI models.
• Needed for inference and consequently, patient data is only as secure as the AI implementation.
Implementation – Legal Concerns
Legal system can’t keep up with the technology
• The FDA is not enamored with online systems.
• Alice Corp. v. CLS Bank International
• Novelty must be found in workflow
Implementation – Legal Concerns
Accountability – What entity is responsible for the AI’s decision?
Implementation – Legal Concerns
Biases – Introduced when creating the model or the result of a model’s inference.
Implementation – Legal Concerns
Transparency – Specifically in deep learning the mechanism of the model is opaque.
Implementation – Ethical Concerns
• Biases – Introduced when creating the model or the result of a model’s inference.
• AI replacing traditional jobs.
• AI used to determine a patient’s medical coverage.
THANK YOU FOR YOUR TIME