Taipei Medical University

AI in Healthcare: Opportunities & Challenges

Taipei Medical University

AI in Healthcare: Opportunities & Challenges

Shabbir Syed-Abdul

Instructor: Shabbir Syed-Abdul

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Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Explain why AI is medicine's fourth revolution, enabling precision, preventive, and patient-empowered care

  • Identify real-world barriers to clinical AI, including bias, interoperability, and explainability.

  • Define the "ideal clinician" who balances AI tools with humanistic empathy and judgment.

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Recently updated!

August 2026

Assessments

9 assignments

Taught in English

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There are 4 modules in this course

This unit explores the necessity of AI as the fourth medical industrial revolution, following breakthroughs like X-rays and antibiotics. It addresses critical gaps in modern medicine, such as the neglect of participatory health, the limitations of "one-size-fits-all" treatments, and medical errors. Students will examine how AI enables a shift from curative to preventive care through precision medicine and patient empowerment, exemplified by Taiwan’s "My Health Bank". Additionally, the unit introduces the evolution and ethical utilization of Large Language Models (LLMs) in academic research.

What's included

5 videos3 readings2 assignments1 discussion prompt

This unit traces the evolution of AI from historical "winters" to modern breakthroughs like Convolutional Neural Networks (CNNs). It highlights the shift from imprecise "one-size-fits-all" medicine—which often leads to diagnostic errors and inconsistent quality—to data-driven precision care. Students will explore common algorithms used for classification, prediction, and optimization across clinical dimensions. The curriculum emphasizes AI’s potential to integrate genotype, phenotype, and environmental data to solve medical imprecision. Finally, the unit frames AI as "intelligent infrastructure" designed to enhance clinical decision-making and correct human errors.

What's included

3 videos4 readings2 assignments1 discussion prompt

This unit examines critical barriers to AI implementation in clinical settings, moving beyond algorithmic patterns to functional understanding. Students will explore Reinforcement Learning (RL) and its unique complexities in high-risk medical environments where trial-and-error is often impossible. The curriculum highlights the challenge of semantic interoperability, addressing how "information-poor" data and a lack of standardized terminology hinder AI performance. Furthermore, the unit dissects the "black box" nature of algorithms, emphasizing the necessity of explainable AI (XAI). Finally, it confronts the "illusion of impartiality," analyzing how machine bias can compromise clinical equity.

What's included

4 videos2 readings2 assignments2 discussion prompts

This unit explores whether the rapid integration of advanced technologies—such as AR, VR, and the Internet of Medical Things (HIoT)—leads to the dehumanization of medicine. Students will examine cutting-edge applications like digital twins and genomic immersion designed to achieve "full doctor-patient immersion". However, the curriculum balances technological optimism with a critical reflection on the personal nature of healthcare, emphasizing that "a fool with a tool is still a fool". Ultimately, the unit defines the "ideal clinician" of the digital age: a practitioner who leverages Augmented Assisted Natural Intelligence (ANI) to enhance safety while maintaining deep empathy and humanistic wisdom.

What's included

4 videos4 readings3 assignments2 discussion prompts

Instructor

Shabbir Syed-Abdul
Taipei Medical University
1 Course1 learner

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