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Week 11: Emerging Technologies and Trends

Section 4: Synthesis | Learning Outcome 4

Theme: Over the horizon: What's coming and why it matters

Core Question: "What comes next?"

Learning Objectives

By the end of this session, students will be able to:

  • Analyse emerging AI technologies and their healthcare potential
  • Evaluate the trajectory of AI development and likely near-term advances
  • Assess the implications of emerging AI for healthcare workforce and practice
  • Develop informed perspectives on AI futures

Content

11.1 Foundation Models and Multimodal AI

Large Language Models: Current State

From ChatGPT to clinical applications: - General-purpose LLMs with medical capability - Rapid capability improvements - Expanding context windows - Improved reasoning and accuracy

Medical Foundation Models

Purpose-built medical AI: - Med-PaLM, BioMedLM, and successors - Training on medical literature and data - Clinical validation efforts - Regulatory pathways

Multimodal AI

Combining data types: - Text + images (clinical notes + imaging) - Multiple imaging modalities - Genomics + clinical data - Physiological signals + context

Healthcare potential: - More complete patient picture - Cross-modal pattern recognition - Integrated decision support

Agentic AI

From question-answering to task completion: - AI that takes actions, not just provides information - Multi-step reasoning and planning - Tool use and integration - Autonomy and oversight challenges

11.2 Technical Advances on the Horizon

Federated Learning

AI training without centralising data: - Data stays at source institutions - Model learns across distributed data - Privacy preservation - Collaborative development

Healthcare implications: - Enables multi-site learning - Addresses data sharing barriers - Supports rare disease AI - Governance challenges

Edge Computing

AI at the point of care: - Processing on local devices - Reduced connectivity dependency - Privacy benefits - Hardware limitations

Applications: - Point-of-care diagnostics - Wearable devices - Remote monitoring - Aeromedical systems

Continuous Learning Systems

AI that improves in deployment: - Learning from new data - Adapting to local patterns - Regulatory challenges - Safety considerations

Digital Twins

Patient-specific simulation: - Virtual patient models - Treatment simulation - Personalised prediction - Training applications

11.3 Emerging Application Domains

Ambient Clinical Intelligence

AI in the consultation room: - Automated documentation - Real-time clinical support - Workflow optimisation - Privacy and consent issues

Autonomous Point-of-Care Diagnostics

Self-contained diagnostic devices: - Lab-on-chip with AI interpretation - Consumer health devices - Remote testing - Quality and safety governance

Predictive and Personalised Medicine

Scaling precision medicine: - Genomics-informed treatment - Individual risk prediction - Treatment response prediction - Equity implications

AI in Mental Health

Growing application domain: - Screening and assessment - Therapeutic chatbots - Crisis intervention - Ethical considerations

Robotics and AI

Physical AI in healthcare: - Surgical robotics - Rehabilitation robots - Care robots - Autonomous vehicles (ambulances)

11.4 Workforce and Practice Implications

How AI May Change Clinical Roles

Potential shifts: - Automation of routine cognitive tasks - Amplification of expertise - New human-AI hybrid roles - Redistribution of work across professions

New Competencies

Skills for AI-augmented practice: - AI literacy and critical evaluation - Human-AI collaboration - Technology-enabled communication - Continuous adaptation

Training and Education

Implications for: - Undergraduate curricula - Postgraduate training - Continuing professional development - Simulation and assessment

AI and Clinical Reasoning

Fundamental questions: - Will AI enhance or atrophy clinical reasoning? - What core skills must be maintained? - How do we train for AI-augmented practice? - What is the role of the clinician when AI excels?

Opportunities and Threats

By specialty and role: - Which areas most affected? - New roles and opportunities - Displacement risks - Transition planning

Aeromedical Thread

Future Retrieval Medicine

Autonomous Vehicles

Self-driving ambulances and beyond: - Current development state - Regulatory pathway - Operational considerations - Human role evolution

Enhanced Remote Diagnostics

Future capabilities: - Advanced point-of-care testing - AI-interpreted imaging - Physiological prediction - Integrated decision support

AI-Augmented Decision-Making in Transit

Evolution of in-flight support: - Real-time specialist consultation via AI - Predictive guidance - Automated documentation - Resource coordination

Evolution of the Retrieval Clinician Role

How roles may change: - From data gatherer to decision validator - From manual documentation to oversight - From protocol follower to exception handler - From isolated practitioner to networked expert

Learning Activities

Pre-Class Preparation

  1. Technology Scan
  2. Research one emerging AI technology in depth
  3. Assess healthcare applications and timeline

  4. Prepare summary for class discussion

  5. Future Perspectives

  6. Read assigned "future of healthcare AI" articles
  7. Note predictions and assess plausibility

In-Class Activities

  1. Emerging Technology Analysis (Groups, 35 mins)
  2. Groups analyse assigned emerging technology
  3. Assess: capability, timeline, healthcare application, barriers

  4. Present and discuss

  5. Scenario Planning Exercise (Groups, 30 mins)

  6. Develop scenarios for AI in healthcare 5-10 years out
  7. Consider optimistic, pessimistic, and likely scenarios

  8. Identify strategic implications

  9. Structured Debate (25 mins)

  10. Motion: "AI will fundamentally change the role of the clinician within 10 years"
  11. Teams argue for and against
  12. Class discussion and synthesis

Post-Class Activities

  1. Future Implications Section
  2. Complete future implications section for capstone project
  3. Include: relevant emerging technologies, trajectory, implications

Indicative Resources

Required Reading

  • Selected recent publications from Nature Medicine, Lancet Digital Health
  • Technology company research publications (Google Health, Microsoft Research)
  • Health workforce futures literature
  • Gartner healthcare technology trends

Session Summary

This week examined emerging AI technologies and their implications:

  1. Foundation models and multimodal AI are expanding capabilities rapidly
  2. Technical advances (federated learning, edge computing) address current barriers
  3. New application domains are emerging across healthcare
  4. Workforce implications are significant and require planning
  5. Aeromedical retrieval will be transformed by these developments

Next Week: In our final session, we'll synthesise the course content and develop your professional position on AI in healthcare.