Ai Innovations With Open Tools In Healthcare Processes

Advanced AI Technologies and Open-Source Tools for Healthcare Delivery, Diagnostics, and Administrative Workflows

What you'll learn

Understand core AI concepts applied to healthcare settings.

Identify real-world healthcare use cases for AI technologies.

Analyze medical data and interpret AI-driven predictions.

Evaluate benefits and risks of AI for clinicians and patients.

Requirements

No prior AI experience required. Familiarity with healthcare workflow helpful.

Access to a computer and stable internet connection.

Curiosity about medical innovation.

Description

Artificial intelligence (AI) is driving a major transformation in healthcare, touching everything from diagnosis to patient management and administrative workflows. Today, open-source AI tools are opening new doors for innovation, making advanced medical technologies more accessible for hospitals, clinics, and researchers. This course, “AI Innovations with Open Tools in Healthcare Processes,” empowers medical professionals, AI engineers, and healthcare researchers to harness ethical, interpretable, and practical AI solutions built on open frameworks.You’ll explore real-world applications of AI—like medical imaging, predictive analytics, and automated documentation—while learning how to overcome challenges such as fragmented data systems (EHRs, wearables) and regulatory compliance (HIPAA, GDPR). Through hands-on labs and interactive projects, you’ll become skilled at using leading open-source tools to build, deploy, and evaluate AI-powered clinical decision support systems that enhance patient care and optimize operational efficiency.No prior coding experience is required; basic healthcare knowledge will help you make the most of this practical course. By the end, you’ll be able to implement cost-effective, scalable AI solutions for diagnostics, monitoring, and reporting, advancing your organization’s healthcare delivery. Join us to explore the future of AI in medicine, transform your professional impact, and become part of a new generation of tech-savvy healthcare innovators ready to lead meaningful change in medical environments globally.

Overview

Section 1: Introduction to Entire Course

Lecture 1 Introduction to the Course

Section 2: What is AI and Why Healthcare Needs It?

Lecture 2 Defining AI in Simple Terms

Lecture 3 Traditional vs. AI-Driven Systems

Lecture 4 Healthcare’s AI Revolution

Section 3: How AI is Already Being Used in Healthcare?

Lecture 5 AI in Imaging: X-ray to MRI

Lecture 6 Predictive AI for Early Detection

Lecture 7 AI in Administrative Workflows

Section 4: Risks, Myths & Opportunities

Lecture 8 Common AI Myths in Medicine

Lecture 9 Clinical Risks of AI Misuse

Lecture 10 Why AI is a Tool, Not a Replacement

Lecture 11 COVID-19 and AI Diagnosis

Lecture 12 Hands-On-Learning: Getting Started with ChatGPT in Clinical Practice

Section 5: Open-Source AI – Principles & Ecosystem

Lecture 13 Intro to Module

Lecture 14 What is Open Source in AI?

Lecture 15 Why Open Tools Matter in Medicine

Lecture 16 Comparing Open-Source vs Proprietary AI

Section 6: Free AI Tools for Clinical Practice

Lecture 17 Using ChatGPT for Clinical Summaries

Lecture 18 Symptoma: AI Symptom Checker

Lecture 19 Glass AI: Differential Diagnosis Helper

Section 7: Datasets & Building AI Without Coding

Lecture 20 Where to Get Medical Datasets

Lecture 21 Teachable Machine for Imaging AI

Lecture 22 Tips for Safe Dataset Use

Lecture 23 10 Top AI Tools in Healthcare for 2025

Lecture 24 Hands-On-Learning: Build a No-Code X-ray Classifier with Teachable Machine

Section 8: AI in Diagnosis & Medical Imaging

Lecture 25 Intro to Module

Lecture 26 How AI Interprets X-rays, CTs, and MRIs

Lecture 27 AI for Dermatology and Histopathology

Lecture 28 Collaboration Between Human Experts and AI Models

Section 9: AI in Monitoring & Risk Prediction

Lecture 29 Forecasting Sepsis, Heart Failure, and Deterioration

Lecture 30 Use of AI in Wearable Devices and Vital Signs Monitoring

Lecture 31 Real-Time Decision Tools and Scoring Models

Section 10: Virtual Assistants and AI in Operations

Lecture 32 Pre-Visit Screening and Education

Lecture 33 Automating Hospital Operations

Lecture 34 AI Supporting Nurses and Doctors with Reminders and Triage

Lecture 35 S.A.R.A.H, a Smart AI Resource Assistant for Health

Lecture 36 Hands-On-Learning: Simulating a Patient Journey with AI Tools

Section 11: Clinical Decision Support Systems (CDSS)

Lecture 37 Intro to Module

Lecture 38 Definition and Importance of CDSS in Hospitals

Lecture 39 Rules vs. Machine Learning-Powered Tools

Lecture 40 Real Scenarios of AI Support in Critical Care

Section 12: AI Safety, Regulation & Legal Issues

Lecture 41 Compliance in AI-Powered Health Platforms

Lecture 42 Who’s Accountable When AI Fails

Lecture 43 Overview of Global Governance Approaches

Section 13: Ethics, Bias & Responsible AI Deployment

Lecture 44 How AI Models Can Reinforce Disparities

Lecture 45 Making AI Understandable to Users

Lecture 46 Principles for Responsible AI Deployment

Lecture 47 AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential

Lecture 48 Hands-On-Learning: Evaluating Bias and Safety in CDSS Tools

Lecture 49 Course Wrap-up Video

Lecture 50 Project: Training a Teachable Machine Model to Detect Pressure Ulcers

Healthcare professionals and students looking to upskill in AI.,Tech enthusiasts interested in healthcare applications.,Administrators, nurses, doctors, and allied health staff exploring AI tools.