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Basics of AI in Healthcare
Rating: 4.3 out of 5(42 ratings)
99 students

Basics of AI in Healthcare

A Guide to AI in Healthcare: Machine Learning, Medical Data, Predictive Analytics, and Real-World Applications
Created bySinjini Bala
Last updated 7/2025
English
English [Auto],

What you'll learn

  • Understand the core concepts of Artificial Intelligence, Machine Learning, and their applications in medical settings.
  • Analyze different types of healthcare data and apply preprocessing techniques essential for AI modeling.
  • Explore real-world use cases of AI in diagnostics, imaging, surgery, mental health, and hospital management.
  • Evaluate the ethical, legal, and social challenges of implementing AI in healthcare, with a focus on fairness, privacy, and accountability.

Course content

2 sections • 12 lectures • 1h 33m total length

  • Introduction to Artificial Intelligence8:34

    1. What is Artificial Intelligence?

    2. Key Concepts in AI: Machine Learning, Deep Learning, and NLP

    3. Brief History of AI in Medicine

    4. Importance and Scope of AI in Healthcare

  • Fundamentals of Machine Learning7:21

    1. Supervised vs. Unsupervised Learning

    2. Common Algorithms: Decision Trees, SVM, k-NN

    3. Training, Validation, and Testing

    4. Model Evaluation Metrics in Healthcare Context

  • Data in Healthcare AI8:06

    1. Types of Healthcare Data (EHRs, Imaging, Genomics, etc.)

    2. Data Preprocessing and Cleaning

    3. Handling Missing and Imbalanced Data

    4. Data Privacy and Security Regulations (HIPAA, GDPR)

  • Natural Language Processing (NLP) in Healthcare8:53

    1. Basics of NLP and Text Mining

    2. Clinical Text Analysis and EMR Interpretation

    3. Named Entity Recognition in Medical Documents

    4. Sentiment Analysis for Patient Feedback

  • AI in Medical Imaging9:00

    1. Overview of Medical Imaging Modalities

    2. Image Segmentation and Feature Extraction

    3. Deep Learning in Radiology

    4. Case Study: AI in Cancer Detection

  • Predictive Analytics and Diagnosis7:52

    1. Disease Prediction Models

    2. Risk Stratification and Early Warning Systems

    3. Real-Time Monitoring with Wearables

    4. Case Study: AI in Predicting Diabetes and Heart Disease

  • AI in Drug Discovery and Development8:03

    1. Virtual Screening and Molecular Docking

    2. AI in Clinical Trial Design and Recruitment

    3. Drug Repurposing Using Machine Learning

    4. Personalized Medicine and Pharmacogenomics

  • Robotics and AI in Surgery7:58

    1. Surgical Robots and Automation

    2. AI-Assisted Decision Support in Surgery

    3. Image-Guided and Minimally Invasive Procedures

    4. Case Study: da Vinci Surgical System

  • AI in Patient Care and Hospital Management7:35

    1. Chatbots and Virtual Health Assistants

    2. Appointment Scheduling and Workflow Automation

    3. AI for Bed Management and Emergency Services

    4. Case Study: AI in COVID-19 Hospital Logistics

  • Ethical, Legal, and Social Implications6:35

    1. Algorithmic Bias and Fairness in Healthcare

    2. Explainability and Transparency in Medical AI

    3. Legal Responsibilities and Liability

    4. Ethical Case Scenarios and Dilemmas

  • Real-World Applications and Case Studies6:39

    1. AI in Oncology: IBM Watson for Cancer

    2. AI in Ophthalmology: Diabetic Retinopathy Detection

    3. AI in Mental Health: Predicting Depression Trends

    4. Global Innovations in AI for Public Health

  • Future Trends and Career Paths7:03

    1. Emerging Technologies: Federated Learning, Edge AI

    2. AI in Rural and Resource-Limited Healthcare

    3. Building a Career in AI and Healthcare

    4. Capstone Project Ideas and Next Steps

Requirements

  • There are no strict prerequisites for this course, it's designed to be beginner-friendly and accessible to learners from both healthcare and technology backgrounds. A basic understanding of healthcare systems or general interest in medical innovation is helpful. No coding or specialized tools are required—just curiosity and a willingness to explore the intersection of AI and healthcare.

Description

Artificial Intelligence is revolutionizing the way we diagnose diseases, manage patient care, and design treatment strategies. In this beginner-friendly course, Basics of AI in Healthcare, you’ll explore how AI, machine learning, and data science are reshaping the future of medicine—one algorithm at a time.

Whether you come from a medical, technical, or research background, this course is designed to give you a solid foundation in how AI tools are used in clinical settings, diagnostics, imaging, hospital management, and beyond. You'll learn about key technologies like machine learning, deep learning, and natural language processing (NLP), as well as practical applications including disease prediction, robotic surgery, drug discovery, and mental health monitoring.

We’ll also dive into real-world case studies—such as IBM Watson in cancer care, AI-powered retinal screening tools, and hospital logistics during the COVID-19 crisis—so you can see the impact of AI in action. Alongside the technical insights, you’ll gain awareness of important issues like algorithmic bias, ethical dilemmas, legal frameworks, and data privacy regulations (HIPAA, GDPR).

No coding skills? No problem! This course focuses on concepts, use cases, and real-world understanding—not programming. By the end, you’ll be equipped to discuss, evaluate, and even contribute to AI-powered healthcare innovations.

So if you’re curious about the future of digital health and want to understand the forces transforming modern medicine—this course is the perfect place to begin.

Who this course is for:

  • This course is ideal for students, healthcare professionals, medical researchers, and tech enthusiasts who are curious about how artificial intelligence is transforming modern healthcare. Whether you're from a medical background looking to understand emerging technologies, or from a tech background aiming to enter the healthcare space, this course offers a comprehensive and accessible foundation. It’s also valuable for public health professionals, policy makers, and anyone interested in the future of digital health and medical innovation.

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