DIPLOMA IN ARTIFICIAL INTELLIGENCE HEALTH CARE PGD (AI He)
In this specialization, we will discuss the current and future applications of AI in healthcare with the goal of learning to bring AiHe technologies into the clinic safely and ethically.
Overview
Artificial intelligence Health care (AiHe) has transformed industries around the world and has the potential to radically alter the field of healthcare. Imagine being able to analyse data on patient visit to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, AiHe could transform patient care and diagnoses.
Ai in Healthcare is an Umbrella Term :
To describe the application of machine learning (ML) algorithms and other cognitive technologies in medical settings. In the simplest sense, AiHe is when computers and other machines mimic human cognition, and are capable of learning, thinking, and making decisions or taking actions. AiHe in healthcare, then, is the use of machines to analyze and act on medical data, usually with the goal of predicting a particular outcome.
A significant AI use case in Healthcare:
Is the use of ML and other cognitive disciplines for medical diagnosis purposes. Using patient data and other information, AI can help doctors and medical providers deliver more accurate diagnoses and treatment plans. Also, AI can help make healthcare more predictive and proactive by analyzing big data to develop improved preventive care recommendations for patients.
This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.
Identify problems healthcare providers face that machine learning can solve.
Analyze how AI affects patient care safety, quality, and research.
Relate AI to the science, practice, and business of medicine.
Apply the building blocks of AiHe to help you innovate and understand emerging technologies.
Healthcare organizations are capturing pet bytes of digital information across electronic health records, genomic sequences. Using this data to improve clinical practice requires AI technologies that bring disparate data together with state-of-the-art accuracy. However, building actionable insights with analytics and machine learning in a clinical setting is no easy task. We focus on how informatics leaders from Health are building a strategy for modern cloud analytics. Common obstacles healthcare organizations face in achieving their strategic goals surrounding big data projects. Best practices for big data from informatics experts .
Opportunities to transform healthcare with AiHe and ML (Machine Learning) Artificial Intelligence Healthcare (AiHe ) has transformed industries around the world and has the potential to radically alter the field of healthcare.
Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, health records, Internet search activity logs that contain valuable health information, get a sense of how AI could transform patient care and diagnoses.
The current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.
Identify problems healthcare providers face that machine learning can solve.
Analyze how AI affects patient care safety, quality, and research.
Relate AI to the science, practice, and business of medicine.
Apply the building blocks of AI to help to innovate and understand emerging technologies.
The Curriculum of the PGD (AIHe) Program
Includes coursework in computing, mathematics, automated reasoning, statistics, computational modeling, introduction to classical artificial intelligence languages and case studies, knowledge representation and reasoning, artificial neural networks, machine learning, natural language processing, vision, and symbolic computation. The program also encourages students to take courses in ethics and social responsibility, with the opportunity to participate in long term projects in which artificial intelligence can be applied to solve problems that can change the world for the better in areas like agriculture, defense, healthcare, governance, transportation, e-commerce, finance, and education.
POST GRADUATE
DIPLOMA ARTIFICAL INTELLEGENCE HEALTH CARE
Scheme of studies (SOS)
PGD (AI He)
| Course Code | Course Title | Credit Hours |
|---|---|---|
| AL HE 301 | Artificial Intelligence in Health Care | 3(3,0) |
| AL HE 302 | Machine Learning in Health Care | 3(3,0) |
| AL HE 303 | Medical Imaging Technologies in AI Healthcare (MI) | 3(3,0) |
| AL HE 304 | Electronic Health Records | 3(3,0) |
| AL HE 305 | Evolutions of AI Applications in Health Care | 3(3,0) |
| AL HE 306 | Beginning Python Programming in Health Care | 3(3,0) |
| AL HE 307 | Fundamental Statistics for Artificial Intelligence in Healthcare (AIHE) | 3(3,0) |
| AL HE 308 | AI: Principles and Techniques | 3(3,0) |
| AL HE 309 | Big Data Challenges & Data Transformation and Dimension Reduction | 3(3,0) |
| AL HE 310 | Artificial Neural Networks | 3(3,0) |
| TOTAL | 30 |
Schedule of courses (Semester Wise)
POST GRADUATE DIPLOMA ARTIFICAL INTELLEGENCE HEALTH CARE
Scheme of studies (SOS)
PGD (AI He)
Semester-I
| Course Code | Course Title | Credit Hours |
|---|---|---|
| AI HE 301 | Artificial Intelligence in Health Care | 3(3,0) |
| AI HE 302 | Machine Learning in Health Care | 3(3,0) |
| AI HE 306 | Beginning & Advance Python Programming in Health Care | 3(3,0) |
| AI HE 307 | Fundamental Statistics (AIHE) | 3(3,0) |
| AI HE 310 | Artificial Neural Networks | 3(3,0) |
Semester-II
| Course Code | Course Title | Credit Hours |
|---|---|---|
| AI HE 303 | Medical Imaging Technologies in AI Healthcare (MI) | 3(3,0) |
| AI HE 304 | Electronic Health Records | 3(3,0) |
| AI HE 305 | Evolutions of AI Applications in Health Care | 3(3,0) |
| AI HE 308 | AI: Principles and Techniques | 3(3,0) |
| AI HE 309 | Big Data Challenges, Data Transformation, and Dimension Reduction | 3(3,0) |
| TOTAL | 30 |
PROGRAM BENEFITS:
The programs are designed to give you the latest, cutting-edge knowledge and skills in the discipline of your study
The Diploma and Postgraduate programs enhance job potential, employability, and career promotion to higher professional positions.
The credits earned in Diploma or PGD programs may be accepted as transfer credits towards Masters’ programs (MA/MSc) at top universities in Europe, USA, or Canada, thereby reducing both duration and cost of earning a master’s degree from a reputed university.
Total Credit Hours: Postgraduate (PGD) 30 Credit Hours.
Modes of Delivery : The courses are offered in the following mode students. Synchronous and Asynchronous online /hybrid learning (Zoom),Microsoft. Google meat
- Classes ( Monday to Friday) 5.30 pm to 8.30 pm
- Mandatory attendance; 85 %
- Passing Prsentage / Subject 70%
- Mandatory appear in examination : Quiz , Assignments, MCQ, Mid-term examination .
Artificial Intelligence Health care(Core)
Core Courses
| Subject / Knowledge Area | Credit Hours | Contact Hours |
|---|---|---|
| Programming for Artificial Intelligence Healthcare | 3(2,1) | 2-3 |
| Artificial Neural Network | 3(2,1) | 2-3 |
| Knowledge Representation & Reasoning | 3(3,0) | 3-0 |
| Natural Language Processing | 3(3,0) | 3-0 |
Electives
| Subject / Knowledge Area | Credit Hours | Contact Hours |
|---|---|---|
| Advanced Statistics | 3(3,0) | 3-0 |
| Deep Learning | 3(3,0) | 3-0 |
| Speech Processing | 3(3,0) | 3-0 |
| Reinforcement Learning | 3(3,0) | 3-0 |
| Programming for Artificial Intelligence Healthcare | 3(3,0) | 3-0 |
| Evolutionary Computing | 3(3,0) | 3-0 |
| Swarm Intelligence | 3(3,0) | 3-0 |
| Agent Based Modeling | 3(3,0) | 3-0 |
| Knowledge Based System | 3(3,0) | 3-0 |
| Robotics | 3(3,0) | 3-0 |
| Statistical Methods for AIHE | 3(3,0) | 3-0 |
| AI in Healthcare Capstone | 3(3,0) | 3-0 |
| Data Science | 3(3,0) | 3-0 |
| AIHE in Practice | 3(3,0) | 3-0 |
| Risk Assessment and Risk Management | 3(3,0) | 3-0 |
| Artificial Intelligence in Pharma and Biotech | 3(3,0) | 3-0 |
| Leading the AI-Driven Organization | 3(3,0) | 3-0 |
| Digital Signal Processing | 3(3,0) | 3-0 |
| Multi-Variable Calculus | 3(3,0) | 3-0 |
