Artificial Intelligence in Medicine HCA 590 (Topics in Health Sciences)

1. Artificial Intelligence in MedicineHCA 590 (Topics in Health Sciences) Rohit Kate 1. Introduction and Overview

2. References • Chapter 1, Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig • Paper: Patel et al. The coming of age of artificial intelligence in medicine. Artificial Intelligence in Medicine (2009) 46, 5—17 • Paper: Ramesh et al. Artificial Intelligence in Medicine. Ann. R. Coll. Surg. Engl. 2004 Sep;86(5):334-8 • Guest Editorial articles from Artificial Intelligence in Medicine journal

3. What is Artificial Intelligence (AI)? • Intelligence, generally speaking, is the ability to do the right or the best thing in a given situation to achieve a goal • Humans and some animals exhibit intelligent behavior; this is intelligence that exists in nature • Artificial Intelligence: Capability of a man-made machine to exhibit intelligent behavior • A chess playing machine • A robot or a car that finds its way around • A computer that answers natural language questions about a topic • A computer that diagnoses a patient from the symptoms • ...

4. What is AI? • Various other definitions of AI are possible ranging from philosophical to engineering perspectives • Machines with minds… • Study of design of intelligent agents… • A good working definition: Study of how to make computers do tasks at which currently humans are better (Rich and Knight, 1991) • AI is a study of duplicating human faculties like creativity, self-improvement and language use • “Artificial” Intelligence may seem to mean that the intelligence is not real or that it just simulates natural intelligence, perhaps a better term could have be “Computational Intelligence” or “Machine Intelligence”

5. What is NOT AI? • Word processing softwares • Graphics design using computers • Entering or searching for an entry in a database • Computer networks, Internet protocols • Computer security • Design of a processor These tasks do not require the computer being used to be intelligent.

6. What Makes Humans Intelligent? • Knowledge • Reasoning • Learning • Language and Perception All these are reflected in the major sub-areas of AI

7. How to Make Computers Intelligent? • Should we model human intelligence • A good idea but difficult to model • Human brain is different from computer processor • Humans are good at remembering and recognizing patterns; computers are good at crunching numbers • A compromising approach: Model human intelligence as much as possible but also utilize computer’s ability to crunch numbers • Airplanes have wings like birds but they don’t flap them, instead they use engine technology

8. Different Sub-Areas of AI • Knowledge Representation and Reasoning • How to encode knowledge and reason from it • Humans do it all the time (common-sense knowledge and reasoning, expert knowledge and reasoning) • First-order logic, ontologies, rules, knowledge-bases • Provision for Uncertain knowledge, probabilistic reasoning • Encode knowledge about diseases, symptoms etc. and then predict diagnosis

9. Different Sub-Areas of AI • Machine Learning • Improve performance by learning from examples • Humans do it all the time (learn to walk etc., develop special skills) • Rule-based methods: e.g. decision-trees • Statistical methods: e.g. neural networks, support vector machines, maximum entropy models • Learn to diagnose a disease from previous examples of patient data

10. Different Sub-Areas of AI • Computer Vision • “Eyes of computer/robot” • Recognize objects (e.g. face, human) from an image • Reconstruct a 3D model from 2D images, e.g. track an object • Distinguish a cancerous from a non-cancerous radiology image

11. Different Sub-Areas of AI • Natural Language Processing • Understand and process natural languages like English, Chinese etc. • Natural language is the preferred medium of communication for humans • Follow natural language commands • Answer natural language questions • Find required information from several documents • Translate from one natural language to another • Answer clinical questions using a repository of research articles

12. Different Sub-Areas of AI • Robotics • Physical agents that act in the physical world • Surgical robots • Planning • Coming up with a best sequence of inter-dependent tasks to perform (e.g. wear socks before shoes) • Planning and scheduling in a hospital environment

13. Turing Test • How to decide whether a machine is intelligent? • Should we make a list of qualities of something that is intelligent? How to come up with such a list? Will everyone agree? • Instead Alan Turing (1950) proposed a test of indistinguishability from humans as an operational definition of intelligence of a machine

14. Turing Test • A machine passes this test if a human cannot distinguish whether it is conversing in writing with a human or a computer behind closed doors

15. Turing Test • To pass the Turing test a machine will need the capabilities of • Natural Language Processing • Knowledge about the world • Automatic Reasoning • Machine learning • Total Turing Test: Also test the subject's perceptual abilities through video and passing physical objects; additional capabilities of • Computer Vision • Robotics

16. Turing Test • Passing Turing test requires capabilities of all the major sub-areas of AI • Loebner Prize: Current contest for restricted form of the Turing test • Usually dominated by hacks to fake human conversations • Not of much interest to real AI researchers • Emphasis of AI research is not in passing this test but on doing well on various tasks that require intelligence, this will eventually lead to a system that will also faithfully pass this test

17. A Turing Test for Medicine?? • A medical doctor can’t distinguish whether conversing with another medical doctor or with a “medically expert” computer

18. Foundations of AI • AI is extremely inter-disciplinary; its foundations come from several older disciplines • Philosophy • Where does intelligence come from? • Mathematics • How to infer logically? How to reason under uncertainty? • Economics • How should we make (intelligent) decisions that maximize payoff ? • Neuroscience • How do brains process information? • Psychology • How do humans and animals think and act? • Linguistics • How does language relate to thought? How do we process language?

19. History of AI • Relatively brief history, only 50-60 years old • Interesting with many ups and downs • A look at its history helps to understand how the current focus and methodologies in AI have emerged

20. History of AI • Beginnings • McCulloch and Pitts (1943) proposed a model of artificial neurons could compute any computable function • Marvin Minsky (1951) built the first neural network computer using vacuum tubes • Alan Turing (1950) introduced Turing test, machine learning, genetic algorithms and reinforcement learning

21. History of AI • Birth of AI • Dartmouth conference (1956) • Organized and attended by some of those who are now regarded as founders of AI: John McCarthy, Marvin Minsky, Allen Newell, Herb Simon • Coined the term “Artificial Intelligence” • Presentation of a reasoning program, "Logic Theorist" which could automatically prove many mathematical theorems

22. History of AI • Early Years (1950s and 60s) • Several interesting and impressive AI work that people earlier did not believe that computers could ever do • General Problem Solver: Could solve limited classes of puzzles thinking like humans • Geometry Theorem Prover: Could prove theorem that were tricky for students • Checkers player: Disproved the idea that computers can only do what they are told to do, soon the program learned to play better than its creator

23. History of AI • Early years (1950s and 60s) • SAINT: Solved freshman calculus problems • ANALOGY: Solved IQ test analogy problems • SIR: Answered simple questions in English • STUDENT: Solved algebra story problems • SHRDLU: Obeyed simple English commands in the blocks world

24. History of AI • Limitations of Early Systems • Could only work on "toy" problems which were not at the scale of real-world problems, for two main reasons • Difficult to formalize and encode real-world knowledge • For example, they tried to build an MT system from Russian to English using dictionaries and syntactic transformations but due to lack of world knowledge: "the spirit is willing but the flesh is weak" -> Russian -> "the vodka is good but the meat is rotten“ • The systems used simple search to find a solution out of all the potential solutions, this would not work with more complex problems which have a combinatorially large space of potential solutions

25. History of AI • Knowledge-based Systems (1970s) • Realization that domain-specific knowledge could help finding the solution led to several expert systems for specific domains • Encoded rules that human experts would used and so these systems could perform like human domain experts • DENDRAL: First knowledge-intensive system to infer molecular structure from mass spectometer data

26. History of AI • Knowledge-based Systems (1970s) • MYCIN: A medical expert system, could diagnose blood infections from symptoms • Encoded 450 rules • Could perform as well as some experts and better than junior doctors • But was never used in actual practice because of non-technical reasons

27. History of AI • AI Industry (1980s) • Several expert systems built and deployed, every major U.S. company had its own AI group to use or to investigate expert system • R1: Helped configure orders for new computers, saved $40 million a year • Japanese started a project to build intelligent computers running Prolog (logic programming language) • In U.S. the company MCC was formed with the same goal

28. History of AI • AI Industry (1980s) • However, limitations of expert systems became apparent • Brittleness (won't work with slightly different input), too domain-specific • Difficult to acquire all the knowledge even for a specific domain (knowledge-acquisition bottleneck) • A brief period of "AI Winter"

29. History of AI • Recent years • Focus on learning from examples to address the knowledge-acquisition bottleneck • To counter brittleness: shift of focus from rule-based and logical methods to probabilistic and statistical methods (e.g. Bayesian networks, Hidden Markov Models) • AI has become a science: • Show real-world applications and not success on toy problems • Base claims on hard experimental evidence and not on intuitions • Analyze results for statistical significance, make data and tools available to replicate experiments

30. History of AI • Recent years • Instead of remaining isolated like early years, AI has embraced other disciplines like statistics, optimization, formal methods etc., whichever areas are needed for success • Increased interest in useful applications at the large scale of the Internet • search engines, recommender systems, Web site construction systems • data mining (find useful patterns in huge amounts of data)

31. State of the Art in AI • Deep Blue beats Kasparov (1997)

32. State of the Art in AI • Spirit, and Opportunity explore Mars (2003)

33. State of the Art in AI • DARPA grand challenge: Autonomous vehicle navigates across desert and then urban environment (2004-2007)

34. State of the Art in AI • Automated speech/language systems for airline travel • Spam filters using machine learning • Usable machine translation through Google

35. State of the Art in AI • IBM supercomputer to compete with human champions on the Jeopardy! TV show • Feb 14-16th 2011

36. AI in Medicine • AI systematizes and automates intellectual tasks and is therefore potentially relevant to any intellectual activity including Medicine • Modern medicine is faced with the challenge of acquiring, analyzing and applying large amounts of knowledge necessary to solve complex clinical problems, AI methods fit this need • AI in Medicine is a subfield of Biomedical Informatics as well as Computer Science

37. Emergence of AI in Medicine • Over the last few years medicine has become a data-rich quantitative field because of various electronic data capturing methods and data management systems for both clinical care and biomedical research, this is transforming medicine from art to science • The availability of data in electronic form (documents, articles, clinical notes, electronic health records etc.) has increased the necessity and scope of their automatic intelligent processing using AI techniques

38. Emergence of AI in Medicine • Diagnosis, treatment and predicting outcome depends on complex interactions of many clinical, biological and pathological variables, hence there is a growing need for analytic tools to analyze them • Note: Many AI in Medicine methods are becoming more and more integrated within medical applications often resulting into their loss of visibility, sometimes not even labeled as AI in Medicine methods; paradoxically, a sign of success

39. AI in Medicine • Applications of AI in Medicine • Help in diagnosis and making therapeutic decisions • Predict outcomes • Support healthcare workers in acquiring, manipulating and searching data • Guide researchers in making discoveries

40. AI in Medicine: Sub-Areas • Knowledge Representation • Design of good ontologies to enable data exchange, standardization, communication, e.g. UMLS, SNOMED-CT, Gene Ontology etc. • Encode rules obtained from domain experts to automate processing and reasoning • Enable discovering new and useful knowledge and refine existing knowledge

41. AI in Medicine: Sub-Areas • Natural Language Processing • Unlock the value buried in text and narrative records so that content can be usedfor automated processing • Interact with computer in natural language, ask clinical questions in natural language to search research articles • Information Retrieval: Find the required information from a collection of documents, answer questions

42. AI in Medicine: Sub-Areas • Decision Support Systems • Help in clinical diagnosis • Combine uncertain evidences from multiple sources and generate a probabilistic diagnosis • Machine Learning • image analysis and segmentation in radiology • data interpretation, waveform analysis • pattern recognition from medical data • Data Mining • Knowledge discovery from databases • Clinical data mining

43. AI in Medicine: Example Systems • MedLEE (http://www.nlpapplications.com/) • MedLEE™ is a Natural Language Processing (NLP)  application that extracts clinical codes from typed or dictated free-text medical narratives and converts the data into computerized clinical information automatically, quickly and error free • Developed at Columbia University • Automates analytics, reporting and alerting for outflows such as Core Measures, PQRI, Patient Summary review, Coding and Claims Adjudication, Decision Support, Clinical Trials, Biological Surveillance and more

44. AI in Medicine: Example Systems • MedLEE (http://www.nlpapplications.com/) • Supports multiple health care systems in the hospital to enhance patient safety, quality assurance, diagnosis and prognosis support, billing and reimbursement administration. The physician is not required to change work habits. • Has been successfully tested by large hospital systems and government agencies, including the New York Presbyterian Hospital, the National Cancer Institute and the U.S. Department of Defense

45. AI in Medicine: Example Systems • GIDEON (http://www.gideononline.com/) • A global infectious disease knowledge management tool • Easy to use, interactive and comprehensive web based tool • Support for the diagnosis and treatment of infectious diseases, knowledge base is updated weekly about diseases and their trends • Hundreds of customers from around the world, including educational institutions, hospitals, public health departments and military organizations, use it as their diagnosis and reference tool for Infectious Diseases, Microbiology and Occupational Toxicology

46. AI in Medicine: Example Systems From: http://www.gideononline.com/

47. AI in Medicine: Example Systems • HELP (Health Evaluation through Logical Processing) • http://www.openclinical.org/aisp_help.html • "HELP was the first hospital information system to collect patient data needed for clinical decision-making and at the same time incorporate a medical knowledge base and inference engine to assist the clinician in making decisions"   [Gardner et al, 1999] • Developed at University of Utah, operational at LDS Hospital in Utah since 1967 • Decision support has been used to provide alerts/reminders, data interpretation, patient diagnosis, patient management suggestions and clinical protocols. • One trial suggested the program had a 94% success rate of choosing an appropriate antibiotic regimen compared to a 77% success rate for physicians

48. AI in Medicine: Example Systems • ATHENA (Assessment and Treatment of Hypertension: Evidence-Based Automation) • http://www.openclinical.org/aisp_athena.html • The ATHENA Decision Support System (DSS) implements guidelines for hypertension using Stanford Medical Informatics architecture • Developed by Stanford Medical Informatics • ATHENA DSS encourages blood pressure control and recommends guideline-concordant choice of drug therapy in relation to comorbid diseases • ATHENA DSS has an easily modifiable knowledge base that specifies eligibility criteria, risk stratification, blood pressure targets, relevant comorbid diseases, guideline-recommended drug classes for patients with comorbid disease, preferred drugs within each drug class, and clinical messages • ATHENA DSS is designed to allow clinical experts to customize the knowledge base to incorporate new evidence or to reflect local interpretations of guideline ambiguities. • See http://www.openclinical.org/aiinmedicine.html for more AI in Medicine systems in practice

49. Adapting AI to Medicine • Medicine is a human endeavor, any AI system needs to take into account human issues like usability, user-friendliness, user-supportiveness, organizational change, workflow etc. • Human expertise in medicine developed over centuries cannot be discarded or replaced by re-discovering them by analyzing data; build models that integrates human expertise with machine learning methods • The methods that can use the existing knowledge and can refine or augment it are preferable over the methods that completely based on data analysis

50. Adapting AI to Medicine • Doctor: We need to amputate your finger. • Patient: Why??? • Doctor: Because our expert system that uses the most hot-shot state-of-the-art statistical machine learning technique says so. • Patient: #@$%&^#