The applications of AI in healthcare include medical imaging, diagnostics, predictive analytics, patient monitoring, personalized care, scheduling, and documentation automation, spanning both clinical and operational needs. This course teaches all seven hands-on in Module 2.

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Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

AI & Digital Transformation in Clinical Practice

Ce cours n'est pas disponible en Français (France)

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AI & Digital Transformation in Clinical Practice

Instructeurs : Ashish Mohan

Inclus avec En savoir plus

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

9 heures à compléter

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

9 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Understand how AI in clinical practice enhances imaging, decision support, and early disease detection.
Explore how digital tools streamlines processes, reduces administrative burden, and improves efficiency.
Learn how AI in healthcare enables predictive analytics and proactive care strategies.
Gain insight into data readiness, clinician adoption, workflow redesign, ethics, and regulatory awareness in clinical operations.

Compétences que vous acquerrez

Catégorie : Patient Observation
Catégorie : Patient Coordination
Catégorie : Health Care Procedure and Regulation
Catégorie : Clinical Documentation
Catégorie : Patient Evaluation
Catégorie : Health Care
Catégorie : AI Enablement
Catégorie : Digital Transformation
Catégorie : Clinical Leadership
Catégorie : Healthcare Ethics
Catégorie : Technology Strategies
Catégorie : Health Administration
Catégorie : Artificial Intelligence
Catégorie : Health Technology
Catégorie : Responsible AI
Catégorie : Diagnostic Tests
Catégorie : AI Integrations

Outils que vous découvrirez

Catégorie : Healthcare Project Management
Catégorie : Risking
Catégorie : AI Workflows

Détails à connaître

Certificat partageable

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août 2026

Évaluations

4 affectations¹

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Il y a 4 modules dans ce cours

Artificial intelligence in healthcare is transforming clinical practice, but successful adoption requires more than understanding the technology. It demands the ability to evaluate AI tools, integrate them into clinical workflows, and use them responsibly to improve patient care. This course equips healthcare professionals with practical, non-technical knowledge to apply AI in clinical practice across diagnostics, patient monitoring, clinical operations, and digital transformation initiatives. Learners will explore how AI supports, rather than replaces, clinical judgment while improving efficiency, reducing administrative burden, and enabling data-driven decision-making.

What makes this course unique is its strong focus on real-world clinical application instead of AI development or programming. Through case studies, workflow redesign exercises, hands-on activities, role plays, and a capstone project, learners will develop the confidence to assess AI in clinical settings, manage organizational change, and address ethical, regulatory, and governance considerations. By the end of the course, learners will be prepared to identify high-impact AI opportunities, lead responsible digital transformation initiatives, and implement AI solutions that enhance patient outcomes while maintaining safety, transparency, and clinician accountability.

This module introduces common clinical and operational challenges in the healthcare industry and discusses how AI and digital technologies can help overcome these challenges. By taking a problem-solving approach, learners can analyze problems like diagnostic delays, administrative burden, and workflow inefficiencies, and understand where AI can help make a difference. The module also explains the use of AI as a clinical assistive technology, managing learners' expectations about what AI can and cannot do. By applying AI concepts to real-world clinical problems, learners establish a practical foundation for understanding the role of AI in today's healthcare delivery environment.

Inclus

11 vidéos2 lectures1 devoir1 évaluation par les pairs2 sujets de discussion

11 vidéosTotal 80 minutes

Course Welcome and Goals6 minutes
Module Introduction5 minutes
Diagnostic Delays and Decision Fatigue9 minutes
Administrative Overload in Healthcare8 minutes
Care Coordination and Workflow Gaps7 minutes
AI as Clinical Decision Support8 minutes
AI in Clinical Documentation and Triage7 minutes
Human-in-the-Loop AI Models7 minutes
What AI Can and Cannot Do8 minutes
Risks of Over-Automation7 minutes
Building Trust in AI Systems9 minutes

2 lecturesTotal 10 minutes

Welcome to the Course: Course Overview5 minutes
AI in Healthcare: Opportunities and Limitations5 minutes

1 devoirTotal 20 minutes

Clinical Challenges and the Role of AI in Healthcare20 minutes

1 évaluation par les pairsTotal 10 minutes

Hands-On-Learning: Mapping Clinical Problems to AI Solutions10 minutes

2 sujets de discussionTotal 15 minutes

Which Clinical Pain Point Should Be Solved First?5 minutes
What Would Make You Trust or Question an AI Recommendation?10 minutes

This module is centered on the application of AI in the various areas of clinical diagnosis, patient care, and healthcare operations. The learners can understand the role of AI in clinical decision support, patient monitoring, and operational efficiency without the replacement of clinical judgment. The practical application of AI is demonstrated in this module through examples, which illustrate the interaction of clinicians and healthcare teams with AI systems, the interpretation of AI output, and the incorporation of AI output into healthcare operations.

Inclus

10 vidéos1 lecture1 devoir1 évaluation par les pairs2 sujets de discussion

10 vidéosTotal 75 minutes

Module Introduction6 minutes
AI in Medical Imaging and Diagnostics8 minutes
Predictive Analytics and Risk Scoring8 minutes
Interpreting AI Outputs in Clinical Decisions9 minutes
AI for Patient Monitoring and Alerts7 minutes
AI Supported Personalized Care Pathways6 minutes
AI in Patient Engagement and Communication6 minutes
AI for Scheduling and Resource Optimization9 minutes
Clinical Documentation and Automation7 minutes
Measuring Operational Impact of AI10 minutes

1 lectureTotal 5 minutes

AI Use Cases in Clinical Practice5 minutes

1 devoirTotal 20 minutes

Applying AI Across Diagnostics, Patient Care, and Clinical Operations20 minutes

1 évaluation par les pairsTotal 10 minutes

Hands-On-Learning: Evaluating AI Use Cases in a Clinical Workflow10 minutes

2 sujets de discussionTotal 10 minutes

Where Would Decision Support Help You Most?5 minutes
Balancing Automation with Human Care in Daily Workflows5 minutes

This module examines the successful implementation of AI and digital technology in the clinical setting. The learners will examine the changes that need to occur in the organization and workflow to implement AI in the clinical setting. This module highlights the importance of change management and building trust among clinicians to implement AI in the clinical setting. By the end of the module, the learners will have gained insight into the implementation of AI-driven digital transformation in the clinical setting.

Inclus

10 vidéos1 lecture1 devoir1 évaluation par les pairs2 sujets de discussion

10 vidéosTotal 76 minutes

Module Introduction6 minutes
What Digital Transformation Really Means in Healthcare7 minutes
Common Reasons AI Projects Fail in Healthcare7 minutes
Aligning AI with Clinical Goals6 minutes
Identifying AI Touchpoints in Clinical Workflows9 minutes
Human–AI Collaboration Models9 minutes
Maintaining Clinical Accountability9 minutes
Clinician Adoption and Trust in AI8 minutes
Training and Upskilling Clinical Teams7 minutes
Measuring Adoption and Transformation Success8 minutes

1 lectureTotal 5 minutes

Digital Transformation in Healthcare Explained5 minutes

1 devoirTotal 20 minutes

Managing Digital Transformation and AI Adoption in Clinical Settings20 minutes

1 évaluation par les pairsTotal 10 minutes

Hands-On-Learning: Redesigning a Clinical Workflow for AI Integration10 minutes

2 sujets de discussionTotal 10 minutes

Where Could AI Fit Naturally in Your Workflow?5 minutes
What Is the Biggest Barrier to AI Adoption in Your Setting?5 minutes

This module covers the ethical, governance, and responsibility issues that are critical to the adoption of AI in a healthcare setting. The learners will explore the impact of issues such as bias, transparency, accountability, and patient trust on the adoption of AI in a healthcare setting. The module also covers the governance and risk management practices that will enable organizations to adopt AI in a responsible manner. At the end of this module, the learners will have the perspective to balance innovation with ethical and patient-centered care.

Inclus

10 vidéos1 lecture1 devoir2 évaluations par les pairs2 sujets de discussion

10 vidéosTotal 73 minutes

Module Introduction5 minutes
Fairness, Bias, and Patient Equity8 minutes
Transparency and Explainability in Clinical AI7 minutes
Accountability and Clinical Responsibility9 minutes
AI Governance in Healthcare Organizations7 minutes
Data Privacy and Patient Consent7 minutes
Risk Assessment and Clinical Safety7 minutes
Building Responsible AI Culture7 minutes
Preparing Clinicians for AI-Driven Futures7 minutes
Balancing Innovation with Patient Trust9 minutes

1 lectureTotal 5 minutes

Ethical AI in Healthcare5 minutes

1 devoirTotal 30 minutes

Ethical, Regulatory, and Responsible AI in Clinical Practice30 minutes

2 évaluations par les pairsTotal 70 minutes

Hands-On-Learning: Ethical Risk Assessment of a Clinical AI System10 minutes
Project: Designing a Responsible AI Adoption Plan for a Clinical Setting60 minutes

2 sujets de discussionTotal 10 minutes

What Safeguard Would You Prioritize First?5 minutes
How Should Healthcare Balance Innovation with Responsibility?5 minutes

Instructeurs

Ashish Mohan

5 Cours2 267 apprenants

Offert par

STARWEAVER

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Foire Aux Questions

Digital transformation in healthcare means restructuring how care is delivered, managed, and coordinated using digital tools and AI, not simply digitizing paper records. Module 3 of this course frames it exactly that way: moving beyond adopting new technology to actually redesigning clinical workflows, building clinician trust, and managing organizational change so AI adoption in healthcare sticks rather than stalling after a pilot project.

Yes. AI & Digital Transformation in Clinical Practice is built specifically for that purpose. Across four modules, it covers how AI in clinical practice addresses real clinical challenges, applying AI to diagnostics, patient monitoring, and operations, managing the digital transformation and adoption process, and navigating the ethical, regulatory, and governance side of responsible AI use. It's practical, non-technical training designed for healthcare professionals, not a programming or AI-development course.

AI is generally classified into seven types: three based on capability (narrow AI, artificial general intelligence, and artificial superintelligence) and four based on functionality (reactive machines, limited memory, theory of mind, and self-aware AI). Most AI in clinical settings today falls under narrow AI and limited-memory AI: systems trained for specific tasks like imaging analysis or predictive risk scoring, not general reasoning. This course focuses on that practical, task-specific category, since it's what's actually deployed in clinical practice today.

The "30% rule" isn't an official standard. It's an informal industry guideline suggesting AI can reliably automate a portion of repetitive, execution-heavy tasks, while judgment and accountability stay with the human professional (the exact split varies by source). This mirrors a core theme of the course: Module 1 directly addresses what AI can and cannot do and the risks of over-automation, while Module 4 reinforces that clinical judgment and accountability remain with the clinician, not the AI system.

Using AI in clinical practice is the central goal of this course, taught through a problem-first approach rather than a technology-first one. Module 1 teaches you to identify where AI can genuinely help, such as diagnostic delays, administrative burden, and workflow gaps. Module 2 shows how to apply it across diagnostics and operations, and Module 3 covers the practical adoption process, including change management and clinician buy-in, so you leave with a plan, not just a concept.

These frameworks vary depending on the source. There's no single universally agreed list of "pillars," "types," or "stages" of digital transformation, since different consultancies define them differently. Rather than teaching a fixed generic framework, this course grounds digital transformation in healthcare in the specific realities of clinical settings: Module 3 covers what digital transformation actually means in healthcare, why AI projects commonly fail, and how to align AI initiatives with clinical goals.

This course is built specifically for healthcare professionals who need practical, non-technical knowledge of AI in clinical practice, not AI developers or engineers. Its focus on real-world clinical application, through case studies, workflow redesign exercises, and a capstone project, sets it apart from more general AI in healthcare or digital transformation courses that aren't grounded in clinical workflows

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.

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