The document discusses the use of artificial intelligence in medical diagnosis, focusing on algorithms that determine system behavior and identify malfunctions. It outlines the project’s aim to serve as a consultant to physicians by matching patient information with a database of possible diseases. Limitations of the project include a lack of knowledge of disease occurrence frequency and pathophysiology, which affects its predictive capabilities.

1. Medical Diagnosis and
Analysis
Abhinandan Patni 10BCE1003
Priyank Trivedi 10BCE1073
Tejaswi Agarwal 10BCE1105
Zeon Trevor Fernando 10BCE1113
Vellore Institute of Technology, Chennai

2. As a subfield in artificial intelligence, Diagnosis is
concerned with the development of algorithms and
techniques that are able to determine whether the
behaviour of a system is correct.
If the system is not functioning correctly, the
algorithm should be able to determine, as accurately
as possible, which part of the system is failing, and
which kind of fault it is facing.
The computation is based observations, which
provide information on the current behaviour.
Some Basic Terminology

3. The expression diagnosis also refers to the answer
of the question of whether the system is malfunctioning
or not, and to the process of computing the answer.
This word comes from the medical context where
a diagnosis is the process of identifying a disease by
its symptoms.
Some basic terminology(contd)

4. About the project

We will focus on how improved representation of clinical
knowledge and sophisticated problem-solving strategies
has advanced the field of artificial intelligence in
medicine.

We will therefore describe the behavior not of a single
existing program but the approach taken by one or
another of the many programs to which we refer.

Our purpose is to provide an overview of artificial
intelligence in medicine to the physician.

5. Design and Methodology
This project is designed to serve as a consultant to
the physician which contains certain basic features.
 It has a store of medical knowledge expressed as
descriptions of possible diseases.
Depending on the breadth of the clinical domain, the
number of hypotheses in the database can range from
a few to many thousands.
The project will be able to match what is known about
the patient with its store of information.

6. Implementation

1. For each possible disease (diagnosis)
determine whether the given findings are to be
expected.

2. Score each disease (diagnosis) by counting
the number of given findings that would have
been expected.

3. Rank-order the possible diseases (diagnoses)
according to their scores.

7. Description

Steps 1 through 3 contain a primitive evaluation
of the available information.

Based on this analysis, the program aims to pin-
point a disease, or in many cases, point to the
possible diseases or disorders that the patient is
affected by.

8. Limitations

It does not take into account how frequently
particular features occur in a given disease.

The program, furthermore, has no knowledge of
patho-physiology and is not able to predict the
severity of an illness.

The most serious problem is that each new
finding sets into motion a search process
tantamount to considering all disease states
appearing in a textbook of medicine.

9. Thank you

10. Thank you