Artificial Intelligence in the Clinical Laboratory

How does it work?

By: Ralph Anderson






Introduction

    I intend to define Artificial Intelligence, and how, when used in the clinical laboratory, AI can be applied to the field of Psychology.  I will also draw a conclusion from this research, including why I choose AI.
 
 

Summery

    Sophisticated software that can monitor and control processes and interpret information, now readily available, can be used "...to incorporate intelligence into advanced analytical instrumentation.  This form of machine intelligence is called Artificial Intelligence." (Place, Truchaud, Ozawa, Pardue, and Schnipelsky, 1994)  The following are points I feel necessary to illustrate what AI is and how it works:
 

    Automaton: an automaton is a machine that is capable of repetitive tasks and uses mechanical forms of feedback.  This is one of the earliest forms of AI, made popular during the Industrial Revolution.  With the introduction of Electronics and Digital Computing, increased memory capacity, sensory technology, and the modern theories of control systems (like AI), it is possible to have robotic arms with full function, space rovers that determine their terrain and adjust accordingly, and anything else you can dream up.  This includes common forms of AI, such as computer Chess.
 

    "Any form of automation or decision making system can be regarded very simply as an input giving rise directly or indirectly via a process system to an output, which then gives rise to some feedback or control." (Place et al., 1994)
 

    Intelligence:  Through the birth of programmable software, the introduction of flexible operational processes and feedback loops to the AI system allowed it the ability to become "Intelligent".  The definition of intelligence states "An individuals overall capacity to think rationally, act purposefully, and deal effectively with the environment." (Coon, 1997), and AI is reliant on the mechanisms of learning.  AI is able to 'learn' by the accumulation of knowledge.
 

    Knowledge: Today, AI and its technologies are generally referred to as "Knowledge Engineering".  The computer software that encompasses this knowledge is called an "Expert System".  Expert systems, through specially programmed, accumulated knowledge bases, are able to make decisions. Drawing on the human expertise that has been meticulously gathered, interpreted, and entered into the system makes these decisions.  Some advantages to this are:

    a) The ability to draw on the expertise of many professionals, simultaneously, to make clear, knowledgeable, logical conclusions.

    b) The cost of the information is considerably lower; the information is already in the computer, so there is no need to tie up dozens of specialized individuals for their opinion.
 

    Inference Engine: "The inference engine implements the problem solving strategies built up by the knowledge engineer...and is independent of the contents of the knowledge base." (Place et al., 1994)  By using an IF-THEN statement, it can determine if a situation is true.  IF-THEN lines state that IF a condition is True\False, THEN perform some action.  Sometimes, however, the information is not completely accurate.  This is most likely to occur in a situation where the amount of knowledge present is insufficient to draw a steadfast conclusion.
 

    Neural Networks: Since decisions are based almost entirely on what is learned by experience, the AI gurus had to devise a different approach, one that sorts out pattern classification and functional synthesis.  Like the human brain, the "...neural network acts as an associative memory, ...good at generalizing from specific examples..."(Place et al., 1994) Neural Networks have to be trained, rather than programmed, by processing time-dependent spatial patterns.  Future AI systems may use a combination of the refined neural network to solve a problem, and an expert system to explain the logical solution provided.
 
 

Applications
 

    The following are just some examples of a stand-alone expert system used in medicine: Thyroid, Colon Cancer, Infectious Diseases, Bacterial Infections, Diabetes, Drug Treatment, and Brain Death.

 

    Medicinal Purposes: With the explosion of knowledge and data in the medical field, the use of a computer-supported medical decision-maker may be the solution.  "Many medical problems, especially those related only to laboratory data, can be solved using conventional algorithms as they contain data of known imprecision.  It has been suggested that only solutions requiring between10 min and 3 hrs of clinician time are suitable for expert systems (Frenzel, LE, 1987)"
 

    Embedded Systems: At many levels, embedded knowledge-based systems are used in laboratory instrumentation.  There are applications for failure, detection, testing and maintenance.  According to Groth and Moden, 1991, they describe a system providing real time quality controller multi-test analyzers using a relational database management system.
 

    Vision: A machines vision is able to detect minute differences that a human cannot.  This is useful in morphologic architecture.
 
 

User Interfaces

    The success of an AI system relies on the user interface, such as:  Keyboard, speakers, microphones, touch pads, the weather and barometric pressure.
 
 

Conclusion

    We have looked at ways in which the development of programmed software and its flexibility has allowed clinical laboratory systems to become intelligent.  We have also looked at how the neural network learns.  This is based on the methods of human learning.  I wonder haw soon it will be before a machine will act, look and think like a human.  What then?  If I was to do an experiment of AI, I would be most interested in the neural networks, and how to improve their learning capability. Someday I hope to be working with cybernetic organisms, and possibly teaching them, and helping them move beyond the "Dr. Spock" state of consciousness.
 
 

Milestones in AI: To see the infancy of AI though it’s adolescence to present day, visit   " Milestones in the Development of Artificial Intelligence" ftp.cs.cmu.edu:/user/ai/pubs/faqs/ai/timeline.txt
 
 

Glossary of Terms  (Parent document is top of "FAQ: Artificial Intelligence Questions & Answers 1/6 [Monthly posting]"  http://www.cis.ohio-state.edu/hypertext/faq/usenet/ai-faq/general/part1/faq-doc-3.html)
 

    AI - Artificial Intelligence: "Any artificial system (often a computer program) that is capable of human-like problem solving or skilled responding." (Coon, 1997)
 

    Admissibility: An admissible search algorithm is one that is guaranteed to find an optimal path from the start node to a goal node, if one exists. In A* search, an admissible heuristic is one that never overestimates the distance remaining from the current node to the goal.
 

    Acquired Strategies: "Learned tactics for swiftly solving the problems encountered in one's area of expertise." (Coon, 1997)
 

    Autonomous Vehicles: Unifies Robotics, Machine Vision, Machine Learning, Intelligent Control, Planning. (Taken from Parent document, top of "FAQ: Artificial Intelligence Questions & Answers 1/6 [Monthly posting]" http://www.cis.ohio-state.edu/hypertext/faq/usenet/ai-faq/general/part1/faq-doc-10.html)
 

    Case-based Reasoning: Technique whereby "cases" similar to the current problem are retrieved and their "solutions" modified to work on the current problem.
 

    Data Mining: Also known as Knowledge Discovery in Databases (KDD) was been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data" in Frawley and Piatetsky-Shapiro's overview.  It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form, which is easily comprehensible to humans.
 

    Expert Systems: "Computer programs designed to respond as a human expert would; programs based on the knowledge and rules that underlie human expertise in specific topics." (Coon, 1997)
 

    Fuzzy Logic: In Fuzzy Logic, truth-values are real values in the closed interval [0.1]. The definitions of the Boolean operators are extended to fit this continuous domain. By avoiding discrete truth-values, Fuzzy Logic avoids some of the problems inherent in either-or judgments and yields natural interpretations of utterances like "very hot". Fuzzy Logic has applications in control theory.
 

    Intelligence: "An individuals overall capacity to think rationally, act purposefully, and deal effectively with the environment." (Coon, 1997)
 

    Knowbots/Infobots and Intelligent Help Desks: Unifies Information Retrieval, Reasoning, Intelligent User Interfaces, Qualitative Reasoning. (Taken from Parent document, top of "FAQ: Artificial Intelligence Questions & Answers 1/6 [Monthly posting]"  http://www.cis.ohio-state.edu/hypertext/faq/usenet/ai-faq/general/part1/faq-doc-10.html)
 

    Nonlinear Planning: A planning paradigm, which does not enforce a total (linear) ordering on the components of a plan.
 

    Organized Knowledge: "Orderly and highly refined information about a particular topic or skill." (Coon, 1997)
 

    Strong AI: Claim that computers can be made to actually think, just like human beings do. More precisely, the claim that there exists a class of computer programs, such that any implementation of such a program is really thinking.
 

    Validation: The process of confirming that one's model uses measurable inputs and produces output that can be used to make decisions about the real world.
 

    Verification: The process of confirming that an implemented model works as intended.
 

    Weak AI: Claim that computers are important tools in the modeling and simulation of human activity.
 
 

References


 

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