AI Case Study Format

Case Study
AI in Healthcare
Disease diagnostics and prediction
Student Name
07_Saumen_Das
21_Chirag_Jha
Guide Name
Savyasaachi Pandit
1) Exclusive Summary
a. Focus on key points, finding, Analysis.

 Epileptic seizure
 Neural Networks
 Machine Learning
 Healthcare application
 Artificial Intelligence
b. Area of Specialization
 Machine Learning
 Data Science
 Neural Networks
 Mathematics
2) Introduction
Artificial intelligence (AI) is defined as the intelligence of machines, as

opposed to the intelligence of humans or other living species. AI can also be
defined as the study of ‘‘intelligent agents”—that is, any agent or device that
can perceive and understand its surroundings and accordingly take
appropriate action to maximize its chances of achieving its objectives.
AI involves a system that consists of both software and hardware. From a

software perspective, AI is particularly concerned with algorithms. An
artificial neural network (ANN) is a conceptual framework for executing AI
algorithms. It is a mimic of the human brain—an interconnected network of
neurons, in which there are weighted communication channels between
neurons.
AI in Healthcare: Disease Diagnosis and Prediction the case studies are

provided to illustrate the prediction of epileptic seizure. In prediction of
epileptic seizure, we summarize the situation analysis, background/history,
objectives, importance of case study, flowchart/model design.
2.1) Situation Analysis
Situation Analysis for Epileptic seizure prediction
2.2)Background / History
 In addition to being able to act as an ‘‘eDoctor” for disease
diagnosis, management, and prognosis, AI has unexplored usage as
a powerful tool in biomedical research.
 On a global scale, AI can accelerate the screening and indexing of
academic literature in biomedical research and innovation
activities.
 In this direction, the latest research topics include tumour-
suppressor mechanisms, protein–protein interaction information
extraction, the generation of genetic association of the human
genome to assist in transferring genome discoveries to healthcare
practices, and so forth.
 Furthermore, biomedical researchers can efficiently accomplish the
demanding task of summarizing the literature on a given topic of
interest with the help of a semantic graph-based AI approach.
 Moreover, AI can help biomedical researchers to not only search
but also rank the literature of interest when the number of research
papers is beyond readability.
 This allows researchers to formulate and test to-the-point scientific
hypotheses, which are a very important part of biomedical research.
For example, researchers can screen and rank figures of interest in
the increasing volume of literature with the help of an AI to
formulate and test hypotheses.
 Some intelligent medical devices are becoming increasingly
‘‘conscious”, and this consciousness can be explored in biomedical
research.
 An intelligent agent called the computational modelling assistant
(CMA) can help biomedical researchers to construct ‘‘executable”
simulation models from the conceptual models they have in mind.
 The CMA is provided with various knowledge, methods, and
databases. The researcher hypothesis is expressed in the form of
biological models, which are supplied as input to the CMA.
 The intelligence of the CMA allows it to integrate all this
knowledge and these models, and it transforms the hypothesis of
the researchers into concrete simulation models.
 The researcher then reviews and selects the best models and the
CMA generates simulation codes for the selected models. In this
way, the CMA enables a significantly accelerated research process
and enhanced productivity.
 In addition, some intuitive machines could guide scientific research
in fields such as biomedical imaging, oral surgery, and plastic
surgery. Human and machine consciousness and its relevance to
biomedical engineering have been discussed in order to better
understand the impact of this development.
2.3) Objectives
 Involves a system that consists of both software and hardware.
 Biomedical information processing.
 Healthcare applications.
 New opportunities for seizure prediction
2.4) Why the case studies important/significant

 To explore the possibility of predicting seizures, which, if made
possible, could result in the development of alternative
interventional strategies.
 Electroencephalography (EEG) plays an important role in detecting

epilepsy, as it measures differences in voltage changes between
electrodes along the subject's scalp by sense ionic currents flowing
within brain neurons and provides temporal and spatial information
about the brain.
3) Model Design
4) Conclusion
It can be seen that AI plays an increasingly important role in biomedicine,
not only because of the continuous progress of AI itself, but also because
of the innate complex nature of biomedical problems and the suitability
of AI to solve such problems.
5) References
[1] Genetic Algorithms

[2] Minsky M. Steps toward artificial intelligence. Proc IRE 1961;49(1):8–30
[3] Epileptic Seizure Detection Based on EEG Signals and CNN
[4] Weng J, McClelland J, Pentland A, Sporns O, Stockman I, Sur M, et al. Autonomous
mental development by robots and animals. Science 2001;291 (5504):599–600.
[5] Hopfield JJ. Neural networks and physical systems with emergent collective
computational abilities. Proc Natl Acad Sci USA 1982;79(8):2554–8.
[6] Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS. Deep learning for visual understanding:
a review. Neurocomputing 2016;187:27–48.
[7] Ben Abacha A, Zweigenbaum P. MEANS: a medical question-answering system combining
NLP techniques and semantic Web technologies. Inf Process Manage 2015;51
(5):570–94
[8] Almeida H, Meurs MJ, Kosseim L, Tsang A. Data sampling and supervised learning for HIV
literature screening. IEEE Trans Nanobioscience 2016;15 (4):354–61.
[9] Negoescu R. Conscience and consciousness in biomedical engineering science and
practice. In: Proceedings of International Conference on Advancements of Medicine and
Health Care through Technology; 2009 Sep 23–26; Cluj-Napoca, Romania; 2009. p. 209–14.
6) Appendices

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Case Study

a. Focus on key points, finding, Analysis.

Artificial intelligence (AI) is defined as the intelligence of machines, as

AI involves a system that consists of both software and hardware. From a

AI in Healthcare: Disease Diagnosis and Prediction the case studies are

2.4) Why the case studies important/significant

 Electroencephalography (EEG) plays an important role in detecting

[1] Genetic Algorithms

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