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AI and Technology in Medical Education - Coggle Diagram
AI and Technology in Medical Education
Introduction
Digital tools are increasingly used in medical education
Online learning
Simulation technologies
Mobile applications
Learning Management Systems
Tele-education
AI-based tools
Technology alone does not improve education
Faculty readiness matters
Proper instructional design matters
Ethical use matters
Alignment with learning outcomes matters
Sadiq et al. 2024
Technology-Enhanced Learning TEL
Includes
E-learning
Blended learning
LMS platforms
Mobile technology
Simulation
Virtual Reality VR
Augmented Reality AR
Provides
Multimedia tutorials
Videos
Educational websites
Podcasts
Online quizzes
Discussion forums
Virtual learning platforms
Main value
Improves access
Supports flexible learning
Helps content delivery
Useful in resource-limited settings
Grimwood and Snell 2020
Digital Tools vs AI
General digital tools
Mainly support content delivery
LMS
YouTube videos
Google Forms
Online lectures
WhatsApp groups
Artificial Intelligence AI
Goes beyond content delivery
Uses data recognition
Uses algorithmic processing
Produces automated outputs
Can personalize learning
AI tracking ability
Tracks search behaviour
Tracks click patterns
Tracks learning activity
Raises transparency concerns
Raises surveillance concerns
Masters 2023
Benefits of AI
Accessibility and flexibility
Distance learning allows students to learn from global experts
MHPE students can learn from home
Saves travel cost
Saves accommodation cost
Supports students in remote areas
Large-scale teaching
Online modules can train large groups
Webinars allow few experts to reach many students
Useful where faculty shortage exists
Low-cost resource-limited solutions
Zero-LMS options
Google Forms for assessment
YouTube videos for demonstrations
WhatsApp for discussion
Downloadable resources
Useful where internet and IT support are weak
Simulation and safe practice
Provides controlled environment
Allows mistakes without patient harm
Role play
Virtual systems
Mannequins
Peer role-play
Supports
Communication
Empathy
Integrated clinical skills
Professionalism
Clinical reasoning
VR and AR
Psychologically safe environment
Mock surgeries in virtual operating rooms
Anatomy visualization
Complex clinical scenarios
Repeated practice
AI-XR integration
Knowledge gains
Skills performance
Task accuracy
Engagement
Personalized and adaptive learning
Adjusts pace
Adjusts content
Personalized feedback
Chatbots for student queries
AI-generated summaries
Individualized learning trajectories
Clinical reasoning and formative feedback
Automated quiz generators
Free AI chatbots
Practice history taking
Practice differential diagnosis
Compare management plans
Instant feedback
Useful for low-stakes formative learning
Engagement and active learning
Clickers increase participation
Online quizzes improve interaction
Discussion forums support participation
Interactive tools improve satisfaction
Active learning increases engagement
Standardization and patient safety
Same learning material across campuses
Same demonstrations for large groups
Same formative assessments
Standardized teaching methods
Students practise before real patients
Reduces patient risk
Backup during disruption
Useful during pandemics
Useful during travel restrictions
Useful during faculty shortage
Useful during clinical placement disruption
Technology is a backup not a full replacement
Bedside Teaching Must Not Be Replaced
Bedside teaching is essential for
Communication skills
Physical examination
History taking
Professionalism
Holistic clinical reasoning
Touch
Human interaction
Empathy
Breaking bad news
Why bedside teaching matters
Real patients cannot be recreated fully
Real pathology cannot be fully simulated
Patient variability is important
Sense of touch is essential
Human judgement is learned in real encounters
Why bedside teaching has declined
Increased reliance on technology
Increased diagnostic testing
Higher patient turnover
Privacy concerns
Time pressure
Balanced position
Use technology for cognitive skills
Knowledge revision
Clinical reasoning
Differential diagnosis
Formative quizzes
Use real patients for psychomotor and affective skills
Physical examination
Empathy
Professionalism
Breaking bad news
Practical integration
AI-generated scripts for history taking practice before real patients
Record bedside conversations with permission
Reflect on empathy and communication
Peer role-play supports clinical reasoning
Bedside remains better for empathy and verbal communication
Ethical Risks and Limitations
Privacy anonymity and surveillance
AI collects large learner data
Passive data collection may occur
Students may not know what is tracked
Search behaviour may be monitored
Clicks may be monitored
Learning activity may be monitored
Weakens anonymity
Consent and data ownership
Students should know what data is collected
Students should know why data is collected
Students should know how data will be used
Nothing about me without me
Data ownership must be clear
Masters 2023
Responsibility and transparency
Who is responsible if AI gives wrong advice
AI may not explain reasoning
Black-box decisions are difficult to trust
Teachers and students need transparency
Bias
Bias in data
Bias in design
Race bias
Gender bias
Cultural bias
US-centred data may not suit Pakistani patients
Outputs may be clinically irrelevant locally
Outputs may be culturally unsuitable
Security
Student data may be breached
Patient data may be breached
Third-party tools may misuse data
Institutions need stronger data protection
Ethical misuse and patient data misuse
Uploading patient data into AI is unsafe
Patient data may be stored
Patient data may be reused
Patient data may be exposed
Violates confidentiality
Damages trust
Cognitive decline and superficial learning
Reduced critical thinking
Overdependence on AI
Copying AI answers without understanding
Shortcut learning
Superficial learning
Erosion of clinical skills
Excessive screen learning weakens physical examination
Simulation cannot fully teach touch
Simulation cannot fully teach real-time judgment
Simulation cannot fully teach patient variability
Loss of human touch
Reduced empathy
Reduced patient connection
Less human interaction
Risk to professionalism
Infrastructure cost and equity issues
Needs LMS
Needs databases
Needs e-portfolios
Needs digital infrastructure
Needs trained faculty
Needs funding
Poor internet in Pakistan
Power cuts
Subscription tools increase inequality
Trust and error risk
Patients may distrust AI-dependent doctors
AI can make factual mistakes
AI can give incorrect suggestions
Students may not identify AI errors without own knowledge
Human knowledge remains essential
Practical Use of AI
Appropriate uses
Low-stakes formative quizzes
Practice questions
Instant feedback
History-taking scripts
Simulated conversations
Differential diagnosis practice
Management plan comparison
Article summaries
Revision support
AI as teaching tool
Critique AI bias
Critique privacy risks
Discuss consent
Discuss transparency
Teach professionalism through AI ethical problems
In clinical learning
AI may suggest possibilities
AI may suggest investigations
AI may support reasoning
Final diagnosis must be by doctor
Human judgement remains central
In assessment
Good for formative assessment
Not suitable alone for high-stakes decisions
OSCEs should remain human judged
Empathy needs human observation
Professionalism needs human judgement
Truthfulness cannot be fully judged by algorithm
Way Forward and Strategic Measures
Balanced integration
Combine AI with bedside teaching
Combine technology with human interaction
Do not fully digitize medical education
Technology should prepare students for patient contact
Technology should not replace patient contact
Educational Ethics Board
Oversee consent
Oversee surveillance concerns
Guide AI use in teaching
Guide AI use in assessment
Guide AI use in research
Faculty development
Train faculty in digital tools
Train faculty in ethical AI use
Ongoing mandatory training
Not one-off workshops
Faculty readiness is essential
Tech buddy system
Pair digitally hesitant senior faculty with tech-savvy juniors
Pair faculty with tech-savvy students
Builds mutual respect
Addresses staffing shortage
Align tools with learning outcomes
Select tools based on educational need
Avoid novelty-driven adoption
Match tool to objective
Use technology for reasoning and revision
Use bedside for examination and empathy
Student AI literacy
Students must critically analyse AI responses
Students must identify AI mistakes
Students must identify bias
Students must check unsupported claims
AI is a supportive tool
AI is not final authority
Avoid superficial learning
Ask students to justify AI outputs
Ask students to critique AI suggestions
Compare AI with textbooks
Compare AI with guidelines
Compare AI with clinical reasoning
Promote deep learning
Low-cost context-sensitive implementation
Google Forms
YouTube videos
WhatsApp
Downloadable videos
Offline resources
Avoid expensive subscriptions where possible
Ensure equitable access
[Local evaluation]
Compare technology-enhanced teaching with traditional teaching
Use student feedback
Use clinical skills assessment
Generate local evidence for Pakistan
Lancet Commission Link
Supports IT-enabled learning
Supports global flow of knowledge
Supports transformative learning
Supports social accountability
Supports health-system strengthening
Warning
Technology should not widen inequity
Technology must serve local health needs
Technology should support change agents
Carnegie Report Link
Supports standardized outcomes
Supports individualized learning
Supports habits of inquiry and improvement
AI can help
Adaptive learning
Feedback
Inquiry
Data-informed improvement
But Carnegie also emphasizes
Integration of formal knowledge with clinical experience
Professional identity formation
Feedback and reflection
Clinical immersion
Therefore
AI must support clinical learning
AI must not replace clinical experience
AI must not replace professional identity formation
Irby Cooke and OBrien 2010