Learn how AI is transforming diagnostics, drug discovery, and surgery, and explore its ethics, challenges, and future
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What you'll learn
- Explain core AI concepts in healthcare, including Augmented Intelligence and key drivers like data growth and computing power.
- Differentiate between Machine Learning techniques (Supervised vs. Unsupervised) and Deep Learning neural networks in medicine.
- Apply Natural Language Processing (NLP) concepts to extract insights from unstructured clinical data and electronic health records.
- Analyze how Convolutional Neural Networks (CNNs) and Computer Vision are revolutionizing radiology and digital pathology diagnostics.
- Evaluate AI's role in accelerating drug discovery, from target identification and virtual screening to optimizing clinical trials.
- Examine how AI enables precision medicine by tailoring oncology treatments and enhancing surgical precision with robotic systems.
- Demonstrate how predictive analytics and AI workflow tools optimize hospital operations, manage patient flow, and reduce clinician burnout.
- Assess the impact of patient-facing AI, including remote monitoring wearables and 24/7 virtual health assistants, on care delivery.
- Identify critical implementation hurdles such as data privacy risks (re-identification), interoperability (FHIR), and high costs.
- Navigate the regulatory landscape for AI medical devices (FDA pathways) and develop strategies to mitigate algorithmic bias in healthcare.
- Evaluate ethical dilemmas in AI healthcare, including the "Black Box" problem, accountability for errors, and patient informed consent.
- Explore emerging trends like Generative AI for synthetic data creation and Federated Learning for privacy-preserving collaboration.
- Define the "Augmented Clinician" concept and the importance of algorithmic literacy in the future evolving medical workforce.