AI in Healthcare: From Fundamentals to Future Practice

A 10-Week Transformational Journey to AI Mastery in Healthcare

Created and led by Dr. Parisa Milne, PhD in Data Science and AI in Healthcare

The Problem: The Missing Middle in Healthcare AI Education

Healthcare professionals today face a frustrating binary choice:

Too Basic: Superficial tutorials that merely scratch the surface offering no real understanding of capabilities or limitations

Too Technical: Dense academic papers filled with algorithm specifications and mathematical proofs that are completely inaccessible to most practitioners.

This gap is preventing healthcare from realising AI's potential:

Clinicians implementing AI tools without understanding their limitations
Valid skepticism turning into wholesale rejection of beneficial technologies
Organisations purchasing AI solutions without the knowledge to evaluate them properly
Growing technological inequality between resource-rich and resource-limited settings

The solution: Bridging the Gap: Our 10-Week Solution

Our programme directly addresses the 'missing middle' by providing:

The crucial bridge between theoretical data science and AI knowledge and practical business implementation. While many professionals understand either the technical aspects OR the business applications, few can effectively translate between these worlds.

We equip leaders and organisations with:

Strategic AI literacy that goes beyond technical jargon
Practical frameworks for identifying high-value AI opportunities
Implementation roadmaps that align with business objectives
Change management strategies for successful AI adoption
Ethical guidelines for ethical and explainable AI

This integrated approach closes the gap between AI potential and realised business value—transforming AI from an intimidating black box into a strategic asset accessible to healthcare decision-makers at all levels.

A Comprehensive 10-Week Learning Journey

📚 Essential Knowledge & Frameworks

Pre-recorded lessons covering all eight core topics
Implementation frameworks specifically tailored to healthcare settings
Curated reading materials and research papers selected for practical relevance
Self-paced quizzes to reinforce and test knowledge retention

🛠️ Hands-On AI Tools & Implementation

Extensive practice with current AI tools to build genuine confidence and competence
Practical prompt engineering instruction for effective AI tool interaction
Hands-on assignments that help internalize concepts through practical application
Development of custom AI projects relevant to your healthcare environment
Guided creation of implementation strategies and business plans for your healthcare idea
Structured evaluation approaches for assessing AI solutions in your context

👥 Community & Collaborative Learning

Connect with healthcare professionals across various disciplines
Community spaces organized around specific discussion topics
Collaborative problem-solving for shared implementation challenges
Ongoing peer-to-peer support that continues beyond course completion

🔴 Weekly Live Sessions & Interaction

Interactive Q&A sessions with Dr. Parisa Milne
Case studies and problem-solving workshops
Guest speakers from healthcare and AI industries
Group discussion of real-world implementation challenges

✍️ Applied Learning & Projects

Weekly assignments designed to help internalize concepts
Practical exercises applying concepts to your organization's specific context
Development of a comprehensive business plan for an AI healthcare solution
Creation of detailed implementation strategies for real-world application

Programme Outcomes

Upon completion, you will be able to:

✓ Bridge the Gap between technical AI capabilities and clinical applications
✓ Evaluate existing healthcare AI systems with a critical understanding of their capabilities and limitations
✓ Select the most appropriate AI models and approaches for specific healthcare problems
✓ Implement AI tools that enhance rather than disrupt clinical workflows
✓ Communicate effectively between technical and clinical stakeholders about AI capabilities
✓ Lead AI initiatives within your organization with both technical understanding and strategic vision
✓ Develop effective strategies for AI adoption that consider ethical implications and bias mitigation
✓ Build custom AI applications tailored to specific clinical or operational needs

Course Topics

Explore these ten comprehensive modules, each designed to build your healthcare AI capabilities:

AI Evolution and Foundation - Understand the historical progression of AI in healthcare, core learning approaches, and the capabilities and limitations of current AI applications in medicine.

A Blueprint for the AI Development Pipeline - Master the complete lifecycle of healthcare AI development, from problem framing and data requirements to deployment in clinical settings.

Bridging Research & Clinical Practice - Learn human-centred design approaches for healthcare AI, build trust in clinical environments, and implement effective change management strategies.

Building Trustworthy Clinical AI - Develop skills in ensuring transparency, reproducibility, and generalizability in AI systems with rigorous evaluation measures for healthcare applications.

Ethical AI & Preventing Clinical Bias and Harms - Identify and mitigate sources of bias in healthcare AI and implement robust ethical frameworks for responsible AI deployment.

Regulatory Landscape and Governance of AI in Healthcare - Navigate global regulatory frameworks, approval pathways, and data privacy regulations while building effective governance structures for healthcare AI.

AI for Healthcare Operations and Efficiency - Optimise hospital workflows, reduce administrative burdens, and implement predictive analytics for improved operational performance.

AI for Healthcare Ventures - Build compelling narratives for AI-first healthcare solutions, identify market opportunities, and develop strategies for scaling successful healthcare AI ventures.

Generative AI for Healthcare - Apply large language models and generative approaches to healthcare challenges with effective prompt design and comprehensive evaluation frameworks.

Specialised Applications of AI in Healthcare - Explore focused applications in drug discovery and development, genomics and personalised medicine, and mental healthcare.

Detailed Course Topics

Module 1: AI Evolution and Foundation

The historical progression of AI and its impact on healthcare
Core distinctions between supervised and self-supervised learning
Understanding neural networks, deep learning, and the rise of large language models
Current capabilities and limitations of AI in medical applications

Module 2: A Blueprint for the AI Development Pipeline

Framing healthcare problems as machine learning solutions
Essential data requirements for building effective ML models
The complete lifecycle: from problem definition to model deployment
Understanding training, validation, and testing methodologies
Navigating the unique challenges of healthcare data and workflows

Module 3: Bridging Research & Clinical Practice

Human-centred approaches to healthcare AI product design
Building transparency and trust in AI systems for clinical environments
Change management strategies for AI implementation
Stakeholder engagement throughout the development process
Integration of patient-generated data and wearables into clinical workflows
Practical implementation of remote monitoring solutions

Module 4: Building Trustworthy Clinical AI

Transparency, reproducibility, and generalizability in AI systems
Rigorous evaluation measures for healthcare AI models
Determining clinical action thresholds based on model certainty levels
Validation approaches for ensuring models work across diverse populations

Module 5: Ethical AI & Preventing Clinical Bias and Harms

Identifying sources of bias in healthcare data, labels, and labelling strategies
Critical assessment of demographic attributes in clinical risk scoring
Development of deployment strategies to mitigate model errors and biases
Ethical frameworks for responsible AI implementation in healthcare
Case studies examining bias detection and mitigation in healthcare AI

Module 6: Regulatory Landscape and Governance of AI in Healthcare

Global regulatory frameworks for AI in healthcare (FDA, EMA, MHRA, etc.)
Navigating approval pathways for AI-enabled medical devices and software as medical devices
Data privacy regulations (GDPR, HIPAA) specific to AI applications
Building robust governance structures for responsible AI deployment
Risk management frameworks for AI in clinical settings
Preparing regulatory submissions and documentation for AI systems

Module 7: AI for Healthcare Operations and Efficiency

Optimising hospital workflows and resource allocation through AI
Reducing administrative burden through automation of routine tasks
AI for healthcare supply chain management and optimisation
Predictive analytics for hospital operations (bed management, staffing, etc.)
Implementation strategies for operational AI solutions
ROI assessment and business case development for operational AI

Module 8: AI for Healthcare Ventures

Building compelling narratives for AI-first healthcare solutions
Identifying platform-product-market fit for healthcare AI innovations
Development strategies for scaling healthcare AI ventures
Building effective interdisciplinary teams for healthcare AI startups

Module 9: Generative AI for Healthcare

Applying large language models and generative AI to healthcare challenges
Designing effective healthcare-specific AI prompts
Utilising multimodal AI models for diverse healthcare data types
Implementation considerations for generative AI in clinical settings
Navigating the limitations and risks of generative AI in healthcare applications
Building comprehensive evaluation frameworks for AI decision support tools

Module 10: Specialised Applications of AI in Healthcare

Part A: AI in Drug Discovery and Development

AI approaches to target identification and validation
Machine learning for drug design and optimisation
AI for accelerating clinical trials (patient selection, monitoring, etc.)
Implementation challenges in pharmaceutical research settings

Part B: AI in Genomics and Personalised Medicine

AI approaches to genomic data analysis and interpretation
Integrating genomic insights into clinical decision support
Building and validating predictive models for personalised treatment
Implementation considerations for precision medicine programmes

Part C: AI in Mental Healthcare

AI applications for mental health screening and monitoring
Development of digital therapeutics and conversational agents
Ethical considerations specific to mental healthcare applications
Implementation approaches for mental health settings

Who Should Apply

This programme is ideal for:

Clinicians (Doctors, Nurses, Surgeons, Radiologists, Pathologists, Pharmacists)
Healthcare Leaders & Executives
Data Scientists and AI Professionals transitioning into healthcare
Researchers in academic and industry settings
Entrepreneurs and Innovators developing AI healthcare solutions
Professionals in Pharmaceutical and Life Sciences companies
Health IT Professionals and Digital Health Startups

Our Unique Value: Why Choose This Programme

How We Compare to University-Based Courses

Personalised Guidance vs. Large Anonymous Cohorts

University Reality: In typical university courses, you're one of hundreds of students with minimal direct access to professors. Questions are often filtered through teaching assistants, and personalised feedback is limited or non-existent.

Our Approach: You receive direct, personalised guidance from Dr. Parisa Milne throughout your learning journey. With our intentionally small cohort size, Dr. Milne knows you by name, understands your specific challenges, reviews your implementations personally, and provides tailored advice based on her extensive experience. This high-touch mentorship simply isn't possible in large university settings where faculty-to-student ratios make individual attention impractical.

Practical Implementation Focus vs. Research Focused

While university programmes often emphasise theoretical frameworks and research methodologies, our course is designed with practical implementation at its core. Every concept is taught through the lens of real-world application, with frameworks and tools you can use immediately in your healthcare setting.

Industry-Led Expertise

Our programme is developed and led by active industry practitioners who bridge clinical environments and technological innovation daily. Dr. Milne brings hands-on experience implementing AI solutions, offering insights that complement academic perspectives with practical wisdom.

Responsive to Rapidly Evolving Technology

Our curriculum is continuously updated to reflect the latest developments in healthcare AI, without the constraints of traditional academic approval cycles. When new tools like advanced LLMs emerge, our content adapts within weeks, not semesters.

Intimate Learning Community with Extended Access

University Reality: University cohorts often number in the hundreds, making meaningful peer connection challenging. Once the semester ends, all community access typically terminates, leaving you without support during the critical implementation phase.

Our Approach: Our programme offers an exclusive mastermind community during the course, plus one full year of post-course access to our closed community of healthcare AI implementers. This extended community access ensures you have ongoing support during the crucial first year of putting your knowledge into practice—precisely when most implementation challenges arise. You'll continue to benefit from peer insights, implementation case studies, and a network of like-minded professionals long after university courses would have ended all engagement.

Customised Implementation Support

Dr. Milne provides individualised implementation support as you apply course concepts to your specific healthcare context. This includes personalised feedback on your projects, targeted resource recommendations, and strategic guidance on overcoming obstacles specific to your organisation—a level of customisation impossible in standardised university curricula designed for mass consumption.

Ongoing Mentorship Beyond Course Completion

Your relationship with Dr. Milne doesn't end when the course does. While university courses typically terminate all engagement at semester's end, our participants receive continued mentorship as they implement AI initiatives, with regular check-ins and guidance to ensure long-term success.

Flexible, Modular Structure

Our learning formats range from comprehensive programmes to focused workshops, allowing you to select the engagement level that suits your needs. University programmes typically require fixed, lengthy commitments regardless of your specific goals.

Direct Access to Multidisciplinary Network

Our community connects you directly with decision-makers across healthcare sectors—from clinical leaders to startup founders to regulatory specialists—creating a valuable network that extends beyond traditional academic boundaries.

The Right Choice For You

Our programme is the ideal choice when you need:

Personalised guidance and mentorship from a leading AI implementation expert
A learning environment where you're known by name, not just a student number
Practical skills you can apply immediately in your healthcare setting or start up idea
Customised support for your specific challenges
A premium community with guaranteed access for a full year after course completion
Flexible learning options tailored to your specific professional goals
Accountability structures that ensure you move from concept to implementation

Many of our participants complement their formal academic qualifications with our practical, implementation-focused approach to achieve the best of both worlds.