AI in Healthcare, What Doctors Must Know About AI

In this video, we introduce you to the core question—what is Artificial Intelligence? We take you through a brief history of AI, then break down the concept into simple, clear terms. You'll learn the difference between AI, Machine Learning, and Deep Learning, and how each one plays a role in modern clinical tools. By the end, you’ll have a strong foundation to understand how AI fits into your daily medical practice—and why it’s becoming essential to learn now.

In this video, we explore what Machine Learning (ML) means and how it powers many of the AI tools used in healthcare today. You’ll learn how ML works step-by-step—from collecting and labeling data to training, testing, and deploying models in clinical settings. We also break down the main types of machine learning—supervised and unsupervised — and connect each one to real clinical use cases. By the end, you’ll see how ML is already shaping diagnosis, treatment decisions, and care delivery.

In this video, we dive into what Generative AI really is and why it’s considered a game changer in healthcare. You’ll learn how GenAI uses large language models (LLMs) to generate human-like responses, summarize clinical notes, and even assist in documentation, patient communication, and education. We explain how it works behind the scenes, and show you the real value it brings to your practice—saving time, reducing burnout, and enhancing decision-making.

In this video, we focus on one of the most important parts of AI—data. You’ll see why good data leads to reliable, accurate AI, while poor-quality data can result in unsafe or biased outcomes. We walk through real clinical examples that show how data quality directly impacts AI performance. You’ll also learn about the different types of data used in healthcare AI—structured, unstructured, images, and more—and why every data point matters when it comes to patient care.

AI isn’t perfect—and in this video, we break down the limitations you need to understand before trusting any tool. We cover issues like biased data, lack of diversity in training sets, hallucinations (when AI makes things up), and overconfidence in results. You’ll also learn why many AI systems struggle to explain their reasoning—and what that means for accountability in clinical care. We wrap up with a forward-looking glimpse into how AI is evolving and what you should watch for as a clinician shaping the future.

Welcome to Module 2 of the course: AI in Clinical Practice. In this module, we move from AI theory to real clinical practice. You’ll explore how AI is already being used across hospitals, clinics, and patient homes—from virtual assistants and clinical decision support tools to imaging diagnostics, wearables, predictive analytics, and personalized medicine. This introduction sets the stage for what’s ahead and highlights the practical, day-to-day value AI brings to modern medical care.

In this video, we explore how AI-powered virtual assistants are supporting patients outside the clinic. From symptom checkers and medical education chatbots to chronic disease coaching and medication adherence tools, you’ll learn how these technologies are improving engagement, reducing unnecessary visits, and helping patients manage their health more effectively while keeping clinicians informed and in control.

This video focuses on how AI-powered tools are supporting doctors in their daily clinical work. You’ll learn how AI assistants summarize patient histories, transcribe consultations in real time, match patients to clinical trials, and keep clinicians up to date with research—all designed to reduce administrative burden, improve accuracy, and let you spend more time focused on patient care.

In this video, we break down how AI-powered Clinical Decision Support Systems (CDSS) work and how they’re being used in real clinical settings. You’ll see how CDSS tools analyze patient data, compare it with clinical guidelines and population-level insights, and deliver real-time, evidence-based recommendations—helping you make safer, faster, and more informed decisions at the point of care.

This video explores how AI is transforming diagnostics across radiology, pathology, dermatology, and ophthalmology. You’ll learn how AI tools analyze medical images with speed and precision, support early disease detection, and help clinicians prioritize urgent cases. Real-world examples show how AI is improving accuracy, reducing diagnostic delays, and expanding access to specialist-level insights—even in underserved settings.

In this video, we explore how wearable devices and home-based medical technologies—powered by AI—are enabling real-time remote patient monitoring. You’ll see how AI interprets data from smartwatches, glucose monitors, ECG patches, and more to detect early warning signs, support chronic disease management, and deliver proactive, personalized care outside the hospital setting.

This video highlights how AI is being used to predict patient outcomes before symptoms appear. You’ll learn how AI models analyze clinical data, lab trends, and real-world risk factors to forecast complications like sepsis, readmissions, or disease progression. With real use cases from ICU care to public health, this session shows how predictive analytics helps clinicians move from reactive to truly preventive medicine.

In this video, we explore how AI enables truly personalized care by analyzing patient-specific data—genetics, clinical history, lifestyle, and treatment response. You’ll see how AI supports precision oncology, risk prediction through genomics, and even accelerates drug discovery. Learn how AI helps clinicians move beyond trial-and-error treatment to deliver more targeted, effective, and individualized care plans.

In this final video of the module, we look ahead at how AI will continue to transform healthcare—from smarter hospitals and predictive systems to personalized care and virtual clinical teams. You’ll explore the expanding role of AI across diagnostics, operations, public health, and research—and understand the vital role clinicians play in guiding its ethical, safe, and effective use. This is your roadmap to staying prepared and leading the future of medicine.

Welcome to Module 3. In this module, we introduce the core themes of AI ethics, legal responsibility, and patient data privacy. As AI becomes part of diagnosis, treatment, and clinical decision-making, it's critical to understand the impact on trust, accountability, and patient care. We’ll explore why ethical use of AI isn’t just a technical concern, but a clinical responsibility every healthcare professional must be prepared for.

This video explores the four major ethical challenges clinicians must consider when using AI in practice: bias, fairness, transparency, and accountability. We break down how each issue can impact patient care, share real-world examples, and explain why healthcare professionals need to stay vigilant when AI becomes part of the clinical decision-making process.

In this video, we explore the global laws and regulatory frameworks that govern AI use in healthcare, including HIPAA, GDPR, and approvals from FDA and EMA. You’ll learn how these regulations protect patient data, ensure clinical safety, and set clear standards for ethical AI deployment—whether you're building your own tools or using third-party solutions in practice.

This video explains when AI is classified as a medical device and what that means for clinical use. We cover how regulators like the FDA and CE Marking authorities assess AI tools for safety, effectiveness, and clinical impact. With real-world examples, you’ll see how AI is now embedded in certified medical devices—and what doctors need to know before relying on them in patient care.

In this video, we focus on how to protect sensitive patient data when using AI. You’ll learn why data privacy is a core clinical responsibility, the risks of breaches, re-identification, and misuse, and the key safeguards—like encryption, access control, and informed consent—that every healthcare professional should look for when using AI tools in practice.

This video explores how wearable devices collect health data and the ethical and legal questions that come with it. We discuss who owns wearable data, when it becomes protected health information (PHI), and what responsibilities doctors have when using it in clinical care. Learn how to navigate consent, privacy, and trust in the age of always-on health tracking.

In this final video, we bring everything together with a step-by-step guide to building ethical AI workflows. From defining the clinical need to ensuring informed consent, regulatory compliance, human oversight, and continuous monitoring, you’ll learn how to integrate AI into practice responsibly. This is your roadmap for using AI safely, transparently, and with patient trust at the center.

In this video, we set the stage for Module 4 by shifting focus from theory to practice. You’ll get a clear overview of how AI moves from concept to clinical reality, covering key stages like problem definition, data strategy, training, deployment, and evaluation. This module empowers you to understand how AI tools are built and what role clinicians play at each step.

This video walks you through the step-by-step strategy for implementing AI in clinical settings. From defining a focused clinical problem and selecting the right data, to involving key stakeholders and setting clear success metrics, you’ll learn how to lay the foundation for safe, effective, and impactful AI solutions in healthcare.

In this video, we explore how different types of healthcare data—structured records, clinical notes, imaging, and wearables—are used by AI. You’ll also learn about key data strategies like Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Federated Learning, and how they power accurate, secure, and real-time clinical insights.

This video covers the critical role of data labeling in training safe and accurate AI tools. You’ll learn how clinical expertise shapes the learning process, how expert-reviewed labels guide model accuracy, and why high-quality, bias-free labeling is essential for building trustworthy AI in healthcare.

This video explains how AI models are trained and validated using labeled clinical data. You’ll learn about training-validation-test splits, supervised learning, model evaluation, and the crucial role of clinicians in reviewing predictions, providing feedback, and maintaining ongoing accuracy through human-in-the-loop systems.

In this video, we walk through the practical steps of deploying AI in healthcare environments. You’ll learn how to integrate AI into EHRs or clinical apps, run pilot tests, train clinical teams, engage patients, and set up ongoing monitoring and feedback systems to ensure AI tools are safe, trusted, and effective in real-world practice.

This video breaks down how to evaluate the real-world performance of AI tools in clinical practice. You’ll learn about clinical metrics like sensitivity and specificity, technical metrics such as precision and F1-score, operational impact, user adoption, patient outcomes, and compliance with ethical and regulatory standards.

This video walks through a real-world case study from Moorfields Eye Hospital and Google DeepMind, showing how an AI tool was developed to detect eye diseases using OCT scans. You’ll see each stage in action—from problem definition and data labeling to training, pilot testing, and clinical impact—bringing the full AI implementation journey to life.

In this final module video, we explore how AI is driving the shift from reactive care to proactive, personalized healthcare. Learn how technologies like remote monitoring, digital twins, multimodal AI, and mental health tools are enabling early detection, prevention, and continuous wellness support—bringing us closer to a smarter, patient-centered future.

In this final video, we wrap up the course by focusing on your role as a clinician in the AI era. You’ll revisit key takeaways, understand how to apply AI knowledge in daily practice, and learn how to stay engaged as a critical voice in shaping ethical, effective, and human-centered AI in healthcare.