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AI in Healthcare: Fundamentals, Analytics & Applications
New
45 students

AI in Healthcare: Fundamentals, Analytics & Applications

Generative AI in Healthcare: Learn Medical AI, Patient Care Automation, Diagnostics, and Real-World Use Cases
Created byYotta Academy
Last updated 8/2026
English
English [Auto],

What you'll learn

  • Understand how Generative AI is transforming the healthcare industry and its real-world impact.
  • Learn key concepts of AI, Machine Learning, and Generative AI in a healthcare context.
  • Explore use cases such as medical report generation, clinical decision support, and patient interaction systems.
  • Understand how AI models assist in diagnosis, drug discovery, and personalized treatment plans.

Course content

2 sections • 16 lectures • 37m total length

  • Introduction to Artificial Intelligence and Machine Learning2:34
  • Key AI Technologies: Deep Learning, Natural Language Processing, Computer Vision2:29
  • Data Science Fundamentals for Healthcare2:12
  • AI vs. Traditional Analytics in Healthcare2:15
  • Real-World Examples - IBM Watson2:17
  • AI-powered diagnostic tools in radiology departments2:15
  • DEMO- Basic machine learning model training using healthcare datasets3:01

Requirements

  • 1. Basic understanding of computers and the internet
  • 2. No prior experience in AI or healthcare is required
  • 3. Interest in Artificial Intelligence and healthcare innovation
  • 4. Basic knowledge of Machine Learning is helpful but not mandatory

Description

This course contains the use of artificial intelligence.

Discover how artificial intelligence and machine learning are transforming healthcare, from clinical applications to hospital operations and financial management.

This beginner-friendly course introduces the foundations of AI in healthcare, including machine learning, deep learning, natural language processing, computer vision, and essential data science concepts. You'll explore how AI differs from traditional healthcare analytics and examine real-world applications of intelligent technologies in healthcare environments.

You'll then learn how AI and predictive analytics can support healthcare management. Topics include patient flow prediction, staff scheduling, resource optimization, quality improvement, and supply chain management. Real-world examples demonstrate how leading healthcare organizations are applying AI to improve operational efficiency and patient services.

The course also explores the growing role of AI in healthcare finance. You'll learn how machine learning can support revenue cycle optimization, fraud detection, compliance monitoring, cost analysis, resource allocation, claims processing, and payer-related workflows.

Practical demonstrations give you hands-on exposure to healthcare AI, including training a basic machine learning model, working with linear models, and exploring fraud detection algorithms.

By the end of this course, you'll have a strong foundation in AI and machine learning for healthcare and understand how these technologies can be applied across clinical, operational, and financial areas of the healthcare industry.

Who this course is for:

  • 1. Healthcare professionals interested in understanding AI applications
  • 2. Students and beginners curious about AI in the healthcare industry
  • 3. AI enthusiasts looking to explore domain-specific applications

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