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  3. AI Innovations with Open Tools in Healthcare Processes

AI Innovations with Open Tools in Healthcare Processes

In this course, you’ll explore how artificial intelligence is reshaping healthcare by combining open-source tools with real-world clinical workflows.

Abdallah Elsokary
Business | intermediate | 4 hours |   Published: Sep 2025

    Discussions

Overview

1KSTUDENTS*
96.8%RECOMMEND*

This course includes:

4 Hours of on-demand video  
Certificate of completion  
Direct access/chat with the instructor 
100% self-paced online

AI is transforming healthcare. Whether it's powering diagnostic algorithms in hospitals, optimizing treatment plans through predictive analytics, or automating administrative workflows in clinics, artificial intelligence is revolutionizing how medical care is delivered. Traditionally, healthcare technology has relied on proprietary, closed systems—but the rise of open-source AI tools is democratizing innovation, enabling researchers, developers, and clinicians to build smarter, more accessible healthcare solutions. 

However, integrating AI into healthcare isn’t just about cutting-edge algorithms—it’s about ensuring these tools are ethical, interpretable, and seamlessly embedded into clinical workflows. This course, "AI Innovations with Open Tools in Healthcare Processes," equips healthcare professionals, AI engineers, and medical researchers with the skills to develop, deploy, and govern AI-driven healthcare solutions using open-source frameworks. 

Skills You Will Gain

AI in Healthcare
Open-source AI tools
Healthcare Automation
Medical AI applications
Ethical AI in medicine

Learning Outcomes (At the end of this program you will be able to)

  • Explain the basics of AI and its practical applications in healthcare.
  • Build and use AI-assisted tools for diagnosis, documentation, and patient monitoring.
  • Build and use AI-assisted tools for diagnosis, documentation, and patient monitoring.
  • Recognize legal, ethical, and safety considerations of using AI in clinical environments.

Prerequisites

Learners should bring basic healthcare knowledge, familiarity with electronic health records, and essential computer skills. No prior coding experience is required. A curiosity about AI’s role in clinical practice will make your journey more impactful.

Who Should Attend

This course is designed for doctors, nurses, clinical educators, and medical interns who want to apply AI in their practice. It also benefits healthcare administrators, researchers, and technologists looking to bridge clinical needs with innovative, open-source AI solutions.

Curriculum

Instructors

Frequently Asked Questions

How much do the courses at Starweaver cost?

We offer flexible payment options to make learning accessible for everyone. With our Pay-As-You-Go plan, you can pay for each course individually. Alternatively, our Subscription-Based plan provides you with unlimited access to all courses for a monthly or yearly fee.

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Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.

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Our course is designed with three levels to cater to your learning needs - Core, Intermediate, and Advanced. You can choose the level that best suits your knowledge and skillset to enhance your learning experience.

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Do you offer refunds?

Yes, we do offer a 100% refund guarantee for our courses within a specified time frame. If you are not satisfied with the course, contact our customer support team to request a refund with your order details. Some restrictions may apply.

*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
Abdallah  Elsokary

Abdallah Elsokary

A highly skilled healthcare technology, informatics, and information security specialist, Abdallah possesses a strong academic foundation in Artificial Intelligence, Information Security, and Nursing Informatics. As a Clinical Nursing Instructor, Certified Ethical Hacker (CEH), Certified EC-Council Instructor (CEI), and Python programmer with over seven years of experience, Abdallah blends technical expertise, cybersecurity knowledge, and clinical insight to bridge the gap between healthcare and digital transformation.

Abdallah has over seven years of nursing experience across intensive care units (ICU), internal medicine, and surgical wards, providing him with deep insight into patient care, hospital workflows, and real-world clinical challenges. This clinical background drives his mission to create practical, patient-centered, technology-driven solutions that improve healthcare delivery.

In addition, Abdallah served as an IT Trainer for three years at the Egyptian Ministry of Communications, where he trained professionals on IT skills and Digital Transformation, preparing them for the demands of an increasingly technology-driven healthcare ecosystem.

A strong advocate for innovation, Abdallah has developed AI-powered tools to detect wounds and pressure ulcers, and to analyze medical data for clinical decision support. He also works on integrating machine learning algorithms, predictive analytics, and automation to optimize EHR systems, streamline workflows, reduce administrative burdens, and enhance patient outcomes.

His expertise in information security enables him to design robust frameworks that ensure data privacy, regulatory compliance, and protection of sensitive patient data from cyber threats.

As a passionate educator and clinical instructor, Abdallah develops and delivers training programs for nurses and healthcare professionals, demonstrating the transformative power of AI, cybersecurity, and digital health. His unique ability to merge academic knowledge, clinical practice, programming, and cybersecurity skills makes him a key contributor to the digital evolution of healthcare.

With a forward-thinking approach, Abdallah continues to explore AI-driven predictive modeling, risk assessments, and automated diagnostics, remaining at the forefront of healthcare technology innovation and committed to shaping a future where AI, nursing education, and informatics revolutionize patient care and decision-making.

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1Module 1: What is AI and Why Healthcare Needs It
2Module 2: How AI is Already Being Used in Healthcare
3Module 3: Risks, Myths & Opportunities
4Module 4: Open-Source AI – Principles & Ecosystem
5Module 5: Free AI Tools for Clinical Practice
6Module 6: Datasets & Building AI Without Coding
7Module 7: AI in Diagnosis & Medical Imaging
8Module 8: AI in Monitoring & Risk Prediction
9Module 9: Virtual Assistants and AI in Operations
10Module 10: Clinical Decision Support Systems (CDSS)
11Module 11: AI Safety, Regulation & Legal Issues
12Module 11: Ethics, Bias & Responsible AI Deployment

Welcome to the Course: Course Overview

Segment 01: Welcome and Course Goals

Segment 02: Module Introduction

Segment 03: Defining AI in Simple Terms

Segment 04: Traditional vs. AI-Driven Systems

Segment 05: Healthcare’s AI Revolution

Segment 06: AI in Imaging: X-ray to MRI

Segment 07: Predictive AI for Early Detection

Segment 08: AI in Administrative Workflows

Segment 09: Common AI Myths in Medicine

Segment 10: Clinical Risks of AI Misuse

Segment 11: Why AI is a Tool, Not a Replacement

Segment 12: COVID-19 and AI Diagnosis

Segment 13: Hands-On-Learning: Getting Started with ChatGPT in Clinical Practice

Segment 14: Risks, Myths & Opportunities

Segment 15: Intro to Module

Segment 16: What is Open Source in AI?

Segment 17: Why Open Tools Matter in Medicine

Segment 18: Comparing Open-Source vs Proprietary AI

Segment 22: Where to Get Medical Datasets

Segment 23: Teachable Machine for Imaging AI

Segment 24: Tips for Safe Dataset Use

Segment 25: 10 Top AI Tools in Healthcare for 2025

Segment 26: Hands-On-Learning: Build a No-Code X-ray Classifier with Teachable Machine

Segment 27: Datasets & Building AI Without Coding

Segment 19: Using ChatGPT for Clinical Summaries

Segment 20: Symptoms: AI Symptom Checker

Segment 21: Glass AI: Differential Diagnosis Helper

Segment 28: Intro to Module

Segment 29: How AI Interprets X-rays, CTs, and MRIs

Segment 30: AI for Dermatology and Histopathology

Segment 31: Collaboration Between Human Experts and AI Models

Segment 45: Compliance in AI-Powered Health Platforms

Segment 46: Who’s Accountable When AI Fails

Segment 47: Overview of Global Governance Approaches

Segment 41: Intro to Module

Segment 42: Definition and Importance of CDSS in Hospitals

Segment 43: Rules vs. Machine Learning-Powered Tools

Segment 44: Real Scenarios of AI Support in Critical Care

Segment 32: Forecasting Sepsis, Heart Failure, and Deterioration

Segment 33: Use of AI in Wearable Devices and Vital Signs Monitoring

Segment 34: Real-Time Decision Tools and Scoring Models

Segment 35: Pre-Visit Screening and Education

Segment 36: Automating Hospital Operations

Segment 37: AI Supporting Nurses and Doctors with Reminders and Triage

Segment 38: S.A.R.A.H, a Smart AI Resource Assistant for Health

Segment 39: Hands-On-Learning: Simulating a Patient Journey with AI Tools

Segment 40: Virtual Assistants and AI in Operations

Segment 48: How AI Models Can Reinforce Disparities

Segment 49: Making AI Understandable to Users

Segment 50: Principles for Responsible AI Deployment

Segment 51: AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential

Segment 52: Hands-On-Learning: Evaluating Bias and Safety in CDSS Tools

Segment 53: AI-Powered Decision Support Systems (CDSS)

Segment 54: Course Wrap-up Video

Segment 55: Project: Training a Teachable Machine Model to Detect Pressure Ulcers