History of AI/medicine by Ted Shortliffe
AI in medicine
AI in image generation - imperfect
Harold Cohen
Created AARON: exhibitions and Whitney Museum Exhibit
Today’s state of the art in medical AI is part of a 60 y scientific trajectory and is still evolving
Origins of AI:
Artificial intelligence coined by John McCarthy
1959: first computer to play checkers
Key leaders in the 1960s: Herb Simon, Allen Newell, Marvin Minsky, John McCarthy
1960-1970: Substantial AI work on problems such as ML/neural networks, NLP, speech understanding, simulation of human problem solving, early robotics
Early AI and Biomedicine - The Dendral Project: Scientific hypothesis formation and discovery/ Encode knowledge of organic chemistry
Example projects
CASNET : causal association network
The MYCIN project: backward chaining expert system
Clinicians will use AI systems if the programs can be shown to function at the level of experts
Certainty factors, evoking strengths in Internist-1, simple “naive” Bayesian models
AI as described in 1982: The ability to reason symbolically, the ability to acquire and apply knowledge, the ability to manipulate and communicate ideas
Explosive interest in AI beyond Academia
1980s 90s: disappointment that applied AI had not rapidly lived up to predictions for imapce, perceived overselling for AI
The AI hype Cycles: periods called “winter AI”
Evolution in technology, methods and applications:
ARPAnet generalized and gradually became commercial (Internet), with introduction of the domain system in late 1980s
Introduction of PCs (1980)
Shrinking size and cost of computer memory
Rapidly increasing computational power, introduction of GPUs
Broadening of application domains
Large databases
Other Ai opportunities: terminology, ontologies, LLMs, NPL
Fascination with Role of Technology and Computing in health care persisted.
Transition to EHR:
Large amounts of digital patient data
New capabilities due to visionary work of computer scientist such as Geoffrey Hinton
Huge opportunity to learn from millions of cases
Medical Device Data interpretation: decision-specific clinical data-> software produces interpretive report-> deliver to clinician-> clinician verifies interpretation and incorporates it into decision making process
Event monitoring and alerts: repository for clinical information on a patient > event monitoring software < knowledge base on what to watch
Direct consultation with Clinical User
clinical advisory tools from:
Analytical methods
repository for clinical information on a patient
-> interface to clinician that integrates decision support with chart review and order entry
Explinable AI: ML models are opaque, non intuitive, and difficult for people to understand
Demonstrably accurate analysis may lead to trust of ai models
Criteria for decision support acceptance and integration into workflow: efficient, intuitive, simple to learn, reflect and understand of the pertinent domain
Is an algorithm derived using deep learning methods new knowledge?
The lecture offered a history of AI and its application to healthcare and medicine. The term "Artificial Intelligence" was coined by John McCarthy (he of LISP and Lambda Calculus fame) in the late 1950s. He coined this with another giant, Claude Shannon, for a conference at Dartmouth. Other pioneers are Herb Simon, Marvin Minsky, and Allen Newell. Through the 60s and 70s, there was a lot of foundational and ground-breaking work done on things we know today as NLP, speech processing, Neural Networks, and Machine Learning (the first 'artificial neuron' was first described in the 1940s, however, and the Perceptron was developed in the late 1950s). By the 1980s, "AI" was generally understood to be a machine/system that many definitions of 'intelligence' could be applied: Acquire and apply knowledge (this is a dictionary definition), manipulate and communicate information/ideas, reason in a symbolic way. However, this decade and the next led to the first "AI Winter" where all the hype and promise of AI sort of fizzled away.
(Some discussion on early efforts at applying AI to healthcare.)
Our times are unlike these however, due to vast improvements in computing power (Moore's Law, GPUs) and the digitization of health records. This has led to new areas of inquiry and application like clinical decision systems.
Discussion on Explainable AI and its importance (e.g. clinicians will 'trust' AI if it can be shown to operate at expert levels). Opaque models help nobody (except VCs/financebros and techbros).