51_Introduction to Artificial Intelligence and its applications.pdf

B.Sc. MLT Syllabus
Attuluri Vamsi Kumar I Assistant professor I Dept of MLT I Ph No: 7416660584 I Website: mltmaster.com
Website: www.mltmaster.com I YouTube: https://www.youtube.com/@vamsiMLT
Program Name: B.Sc. Medical Lab Technology
Syllabus of Introduction to Artificial Intelligence and its applications
Course Name: Introduction to Artificial Intelligence and its applications Course Type
Course Coordinator: Attuluri Vamsi Kumar – B.Sc. MLT, M.Sc. MLT, PhD (Perusing)
Designation: Assistant Professor
Department: Department of Medical Lab Technology (MLT)
Ability
Enhancement
PRE-REQUISITE Basic life sciences up to 10+2 level Sem: 7 S. No: 51
About Vamsi: I am academician in Medical Laboratory Sciences with a strong desire to improve Outcome
based education (OBE) structured MLT education. I am constantly focusing on building an academic
atmosphere that is set high standards with strong multi blended teaching pedagogy models.
YouTube: https://www.youtube.com/@vamsiMLT
SlideShare: https://www.slideshare.net/VamsiIntellectual
Website: www.mltmaster.com / https://sites.google.com/view/vamsi-intellectual-protfolio/home
LinkedIn: https://www.linkedin.com/in/vamsi-kumar-attuluri-ab8987128/?originalSubdomain=in
Research Gate: https://www.researchgate.net/profile/Attuluri-Kumar
Orcid ID: https://orcid.org/0000-0001-9278-6714
Contact No: +91 7416660584
Mail ID: vamsifmlt@gmail.com
A. COURSE DESCRIPTION
This course provides an in-depth understanding of the fundamentals, applications, and future
trends of artificial intelligence (AI) in the field of medical lab technology. It covers the role of
AI in clinical lab diagnostics, predictive analysis, big data interpretation, precision medicine,
and ethical considerations in AI deployment. Through case studies, students will gain practical
insights into the use of AI in healthcare.
B. COURSE OBJECTIVES:
1. Define and describe the basics and history of artificial intelligence, its applications in clinical lab
technology, and the roles it plays in lab diagnostics.
2. Explain the concepts of machine learning and predictive analysis, including their algorithms and
their applications and limitations in medical labs.
3. Apply the knowledge of big data analytics in interpreting lab data and understand its significance
in healthcare.
4. Analyze the role of AI in precision medicine, gene editing, and genetic counseling, and understand
how it contributes to personalized treatment.
5. Evaluate the ethical considerations associated with AI in medical lab technology, including privacy
concerns, AI bias, and inequalities in healthcare.
C. COURSE OUTCOMES
CO
No
Statement Performance
Indicator
Level of
Learning
(Highest BT
Level)
Target
Attainment
CO1 Students will be able to recall the key concepts and
principles of artificial intelligence and its
applications in medical lab technology.
PI 1, PI 6 K1
(Remembering)
70%
CO2 Students will understand the role and applications of
machine learning, predictive analysis, and big data
in medical lab diagnostics.
PI 2.1, PI 2.5,
PI 2.6, PI 2.8,
PI 3, PI 4, PI 7
K2
(Understanding)
70%

B.Sc. MLT Syllabus
CO3 Students will be capable of applying AI principles
to interpret and manage big data in a lab diagnostic
setting.
PI 2.2, PI 2.3,
PI 2.4, PI 2.7,
PI 7, PI 8
K3 (Applying) 70%
CO4 Students will have the ability to analyze the impact
and role of AI in precision medicine and its
significance in personalized treatment.
PI 2.5, PI 2.6,
PI 3, PI 6
K4 (Analyzing) 70%
CO5 Students will be capable of evaluating the ethical
issues, including privacy concerns, bias, and
inequalities, that arise with the use of AI in medical
lab technology.
PI 1, PI 9, PI
10
K5 (Evaluating) 70%
CO6 Students will be able to create a comprehensive
understanding of future trends in AI for medical lab
diagnosis and use this knowledge to suggest
improvements in current healthcare practices.
PI 5, PI 6, PI 7,
PI 8, PI 9
K6 (Creating) 70%
D. SYLLABUS
Unit-1 Introduction to Artificial Intelligence in Medical Lab
Technology
Contact Hours:15
Chapter 1.1 Basics of Artificial Intelligence- What is Artificial Intelligence, History of AI, Importance of
AI in Healthcare and Lab Diagnosis
Chapter 1.2 Fundamentals of AI in Lab Technology- AI Applications in Clinical Lab Technology, Role
of AI in Lab Diagnostics, Challenges in AI Deployment in Clinical Labs
Experiment 1 /
Case study
Case Study 1: Implementing AI for Faster Blood Test Analysis
Experiment 2 /
Case study
Case Study 2: Use of AI for Automation in Microbiology Labs
Experiment 3 /
Case study
Case Study 3: AI in Genomic Sequencing and Analysis
Unit-2 Advanced Concepts of AI in Medical Lab Diagnosis Contact Hours:15
Chapter 2.1 Machine Learning and Predictive Analysis in Lab Diagnosis - 51
Chapter 2.2 AI and Big Data in Medical Labs- Understanding Big Data in Healthcare, Big Data Analytics
in Lab Diagnostics, Role of AI in Interpreting Lab Data
Experiment 4 /
Case study
Case Study 4: Predictive Analysis for Cancer Diagnosis
Experiment 5 /
Case study
Case Study 5: AI in Clinical Decision Support Systems
Experiment 6 /
Case study
Case Study 6: Big Data Analytics for Pathogen Detection
Experiment 7 /
Case study
Case Study 7: Role of AI in Genetic Research and Personalized Medicine
Unit-3 Future Trends in AI for Medical Lab Diagnosis Contact Hours:15
Chapter 3.1 Role of AI in Precision Medicine- Understanding Precision Medicine, Role of AI in
Personalized Treatment, AI for Gene Editing and Genetic Counseling
Chapter 3.2 Ethical Considerations in AI for Medical Lab Technology- Privacy Concerns in AI and Big
Data, AI Bias and Inequalities in Healthcare, Ethical Guidelines for AI in Healthcare
Experiment 8 /
Case study
Case Study 8: AI in Precision Oncology: Towards Personalized Cancer Treatment
Experiment 9 /
Case study
Case Study 9: AI in Gene Therapy and Rare Diseases Diagnosis
Experiment 10 /
Case study
Case Study 10: Ethical Challenges of AI in Healthcare: An In-depth Analysis
Self-study topics for Advance learners: Advanced Machine Learning Algorithms in Medical
Diagnostics: This topic can delve deeper into complex machine learning algorithms such as Deep

B.Sc. MLT Syllabus
Learning, Neural Networks, and Reinforcement Learning, exploring their applications and
effectiveness in medical diagnostics.,Natural Language Processing (NLP) in Medical Diagnostics:
Explore how NLP can be used in analyzing patient records, lab reports, clinical notes, and more to
extract meaningful information and assist in diagnostics., AI in Genomic Medicine: Understand how
AI is revolutionizing Genomic Medicine, from predicting disease susceptibility to personalizing
treatments., AI in Radiomics: A deep dive into how AI is being used in Radiomics for extracting
features from radiographic medical images to improve diagnostic accuracy., AI in Pathomics:
Exploring the use of AI in pathology, specifically in the automation of pattern recognition in
histopathological images.
E. TEXT BOOKS/REFERENCE BOOKS
TEXT BOOKS
T1 "Artificial Intelligence in Medicine: A Practical Guide" by Lei Xing, Maryellen L. Giger,
James K. Min
T2 "Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again" by Eric
Topol
T3 "Medical Laboratory Science Review" by Robert R. Harr
REFERENCE BOOKS
R1 "Artificial Intelligence in Healthcare" by Adam Bohr, Kaveh Memarzadeh
R2 "The Fourth Industrial Revolution: Ai, Data Science, Blockchain, Iot, and more" by Klaus
Schwab
R3 "Artificial Intelligence: Structures and Strategies for Complex Problem Solving" by
George F. Luger

B.Sc. MLT Syllabus
Code Performance Indicators
PI 1 Demonstrate professional interpersonal, oral, and written communications skills sufficient to
serve the needs of patients and the public including an awareness of how diversity may affect
the communication process.
PI 2 Perform pre-analytical, analytical, and post-analytical processes:
PI 2.1 Demonstrate ability to understand investigation/test requisition.
PI 2.2 Collecting the relevant clinical samples along with complete and accurate documentation with
proper safety measures in relation to sample accountability.
PI 2.3 To transport the samples with precautionary measures to the relevant lab section.
PI 2.4 Demonstrate the ability to prepare clinical sample for processing
PI 2.5 To demonstrate the knowledge of accurate sample processing for the required lab
investigation. Perform routine clinical laboratory tests in clinical chemistry,
haematology/haemostasis, immunology, immunohematology, microbiology, Histopathology,
Cytopathology, body fluid analysis, and laboratory operations.
PI 2.6 Perform mathematical calculations related to all areas of the clinical laboratory
PI 2.7 Ability to record the test results/data.
PI 2.8 To demonstrate the ability to interpret the test reports and its documentation in lab records.
PI 2.9 Demonstrate ability to release the report to the right person in minimum turn-around time
(TAT).
PI 3 Perform problem solving and troubleshooting techniques for laboratory methodologies
Correlate laboratory test results with patient diagnosis and treatment.
PI 4 To follow basic quality assessment protocol of clinical laboratory.
PI 5 Demonstrate routine laboratory techniques sufficient to orient new employees within the
clinical laboratory.
PI 6 Apply basic scientific principles in learning new techniques/procedures; demonstrate
application of principles and methodologies.
PI 7 Utilize computer technology applications to interact with computerized instruments and
laboratory information systems.
PI 8 Demonstrate adequate knowledge of computer software as it applies to document production,
spreadsheets, and presentations.
PI 9 Demonstrate professional behaviour with co-team mates.
PI 10 Demonstrate sensitivity and compassion towards patients.

51_Introduction to Artificial Intelligence and its applications.pdf

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51_Introduction to Artificial Intelligence and its applications.pdf