Course Overview: Artificial Intelligence in Healthcare

Artificial Intelligence (AI) is playing an increasingly important role in the healthcare sector by enhancing data interpretation, improving operational efficiency, and supporting informed decision-making. The Artificial Intelligence in Healthcare – Full Course offers a comprehensive introduction to how AI is being used across various aspects of the healthcare system.

This course is designed for learners from medical, life science, and technology backgrounds who wish to explore how AI contributes to healthcare workflows. It focuses on the core principles of AI, including machine learning, natural language processing, and computer vision, and how these technologies are applied in real-world healthcare settings.

Learners will explore topics such as AI in medical imaging, diagnostics support, hospital management systems, patient monitoring, public health data analytics, and digital health tools. The course also highlights the importance of ethics, data privacy, and regulatory frameworks while adopting AI technologies in sensitive medical environments.

Through case-based discussions and conceptual learning, participants will gain an understanding of the opportunities and challenges associated with implementing AI in healthcare. No prior programming or technical expertise is required, making the course accessible to a broad audience.

The course does not provide clinical training or promise outcomes such as job placement, certification for practice, or academic credit. Its goal is to build awareness, encourage critical thinking, and introduce responsible AI practices in the healthcare context.

Whether you are a healthcare professional, student, researcher, or simply interested in the future of medicine and technology, this course offers a clear and structured foundation for understanding AI’s growing presence in healthcare.

Participants who complete all modules and assessments will receive a Certificate of Completion, serving as a record of their participation.

What Will Students Learn in This Course?

• Understand the fundamentals of Artificial Intelligence and its relevance in healthcare.
• Explore how machine learning is used in diagnostics, predictions, and patient monitoring.
• Learn the basics of natural language processing and its role in medical data analysis.
• Discover how AI supports medical imaging and radiology interpretation.
• Understand the use of AI in hospital resource planning and administrative workflows.
• Gain insight into ethical issues, data privacy, and regulatory challenges in AI healthcare.
• Learn about AI applications in public health, epidemiology, and remote care tools.
• Explore real-world case studies highlighting AI use in healthcare systems.
• Get familiar with non-coding tools and platforms used in healthcare AI applications.
• Build a foundational understanding to explore further learning or interdisciplinary research.

What Are the Requirements or Prerequisites for Taking This Course?

• Learners should have a general interest in healthcare or technology, and the ability to understand conceptual frameworks across disciplines.
• No background in coding or artificial intelligence is necessary; the course is designed for beginners with clear, easy-to-follow explanations.
• Comfort with navigating online learning platforms, watching videos, and attempting quizzes or reflective exercises is expected.
• A working knowledge of English is helpful as the course materials, instructions, and discussions are presented in English.

Who Is This Course For?

• Medical, nursing, pharmacy, and public health students interested in tech-driven healthcare.
• Healthcare professionals seeking to understand AI’s potential in clinical and administrative settings.
• Researchers and educators looking to integrate digital health concepts into their work.
• Anyone interested in how AI is transforming healthcare practices globally.

Certification

Participants who complete the course requirements will receive a Certificate of Completion.
This certificate serves as a record of participation and learning.