Course Content
Phase 1: Introduction to AI in Medicine (Week 1-2)
Objective: Provide foundational knowledge on AI, NLP models, and their applications in healthcare.
0/6
Introduction to the course
16:27
Session 1: Overview of different AI tools in Healthcare
39:35
Assignment 1
Session 2: Understanding user interfaces and Prompt structuring
52:15
Assignment 2
Phase 1 Quiz
Phase 2: Using ChatGPT and DeepSeek in Clinical Practice (Week 3-5)
Objective: Train doctors in utilizing AI for patient care, diagnostics, and decision support
0/6
Session 3: Clinical Decision Support with AI + Using AI for differential diagnoses and treatment guidance
40:20
Assignment 3
Session 4: Enhancing Communication with AI + AI powered patient education & consent explanation
53:05
Session 5: AI in Study and Education
31:05
End of phase Assignment (4), watch this video and write 200 words as a reflection on it
Phase 2 Quiz
Phase 3: Leveraging AI in Medical Education, research & Training (Week 6-8)
Objective: Train doctors to use AI for self-learning, student education, scientific research and simulation-based training.
0/8
Session 6: Use of AI in Medical research
23:55
Assignment 5
Session 7: Teaching & Medical Simulation: Creating interactive clinical case scenarios with AI
01:02:07
Assignment 6
Session 8 and 9: AI in Literature Review, CPD and Medical Conferences, Enhancing journal club discussions with AI generated insights
48:27
Assignment 7
Session 10: AI in Workflow Automation & EHR
38:50
Phase 3 Quiz
Phase 4: Ethical, Regulatory, Future Applications of AI in Medicine and Capstone project (Week 11-14)
Objective: Address the responsible use of AI in healthcare, future trends, and implementation strategies.
0/7
Session 11: Introduction session for Capstone project
28:05
Session 12: Capstone project
20:12
Session 13: Future of AI in Medicine, Open discussion
Capstone project
Session 14: Ethical and Regulatory Aspects of AI in Healthcare
Session 15: Summary of key takeaways AI competency assessment
Assignment
AI for Healthcare professionals (1st edition)
About Lesson

SUMMARY of SESSION

Welcome to the AI for Healthcare Professionals course. This introductory session aims to guide participants through the evolving world of artificial intelligence in healthcare. The course covers various AI tools currently used in the sector, key concepts, and a balanced discussion of their potential and limitations.

It begins with a disclaimer emphasizing that the course is for educational purposes only. AI tools discussed should never replace clinical judgment, professional guidelines, or regulatory standards. Users are encouraged to rely only on peer-reviewed, research-validated tools, as the field is still emerging.

The session traces the historical development of computing, from the ENIAC in the 1940s to personal computers in the 1970s, the internet in the 1990s, smartphones in the 2000s, and most recently, the rise of generative AI in 2022 with the launch of ChatGPT. This marked a transformative moment, particularly relevant to medicine and healthcare.

The course introduces major AI tools such as ChatGPT, DeepSeek, Gemini (by Google), Manus, Claude, Microsoft CoPilot, Perplexity AI, and OpenEvidence. These tools have various applications, from coding support to evidence summarization and clinical decision-making.

A recent publication from the Journal of Clinical Medicine is referenced to highlight four major sectors where AI is currently making an impact in healthcare:

1. Operational Efficiency – Improving patient flow, logistics, and resource management.

2. Imaging and Diagnostics – Enhancing analysis in modalities like X-rays, MRIs, and ultrasounds; supporting early disease detection.

3. Workflow Automation – Streamlining documentation, administrative tasks, and enabling real-time data entry.

4. Translation and Communication – Supporting cross-language communication through real-time translation and document conversion.

AI is also revolutionizing treatment personalization, remote monitoring, and real-time interventions, improving overall patient safety and outcomes.

An informal survey was conducted within a WhatsApp group of over 350 healthcare professionals involved in AI. The majority reported no sanctions from medical regulatory bodies (like the GMC in the UK) related to AI use. However, there have been isolated cases highlighting the need for careful and compliant adoption of AI in clinical practice.

Participants are encouraged to view themselves not just as users but also as potential contributors to the field of medical AI-whether as validators, co-creators, educators, or innovators. One does not need coding skills to make a difference; healthcare professionals can define problems, validate AI outputs, design custom tools using no-code platforms, or even advocate for responsible AI integration within their institutions.

Roles like Clinical Safety Officer, which are being increasingly recognized and funded in healthcare systems like the NHS, are highlighted as opportunities for those interested in bridging clinical and digital expertise.

Finally, the course invites ongoing feedback and interaction through WhatsApp groups and email, encouraging participants to help shape future sessions and content. The next session will focus on a detailed overview of the different AI tools currently in use in healthcare.

 

Next Session will be covering

Welcome to the first session of the “Artificial Intelligence for Healthcare Professionals” course. This session provides an overview of various AI tools used in healthcare, explores essential AI concepts, and highlights both the benefits and limitations of these technologies.

The main objectives of this session include understanding the core concepts of artificial intelligence, including the distinctions between machine learning, deep learning, natural language processing (NLP), large language models (LLMs), and generative AI. It also explores key healthcare tools powered by AI, such as ChatGPT, DeepSeek, and Manus. Notably, Manus was only recently released, in March.

The session introduces ethical implications associated with AI use in healthcare and includes a practical assignment for participants to complete at their own pace.

Key definitions and concepts:

  • Artificial Intelligence (AI) refers to systems that mimic human intelligence by performing tasks such as learning, reasoning, and understanding language.

  • Machine Learning involves teaching a system to recognise patterns and make decisions based on data.

  • Deep Learning is an advanced form of machine learning using neural networks for complex data analysis, such as facial recognition and autonomous driving.

  • Natural Language Processing (NLP) allows machines to interpret and respond to human language, a technology used in voice assistants and chatbots.

  • Large Language Models (LLMs) are AI systems trained on vast datasets to understand and generate language, including ChatGPT, DeepSeek, Gemini, and Manus.

  • Generative AI refers to AI that creates new content, such as text or images, based on user prompts. However, generated content can contain inaccuracies or “hallucinations.”

Practical demonstrations compared ChatGPT and DeepSeek’s capabilities in generating and analysing content. These comparisons revealed that some outputs may contain incorrect information, such as inaccurate logos or fabricated details. This underscores the importance of reviewing and verifying AI-generated outputs, especially in healthcare.

The session then explored AI applications in various healthcare areas:

  • Clinical Decision-Making and Workflow Automation: AI chatbots such as Babylon and Ada Health assist with triage and symptom assessment. AI-powered electronic health records (e.g. Nuance DAX, Epic AI) streamline documentation.

  • Radiology and Imaging: AI supports image interpretation in x-rays, CT scans, and MRIs, improving diagnostic accuracy and speed.

  • Cardiology and Ultrasound: AI tools assist with ECG interpretation and ultrasound image analysis, enhancing both clinical and educational uses.

  • Pathology: Tools like PathAI aid in slide analysis and diagnostic accuracy.

  • Medical Education and Simulation: AI supports adaptive learning and advanced simulation tools, such as those developed by GE and OpenEvidence.

  • Critical Care and Anaesthesia: AI models (e.g. CLEW Medical, DeepMind Streams, Max Sleepy) help optimise patient monitoring and sedation.

  • Drug Discovery and Research: Systems like DeepMind’s AlphaFold and IBM Watson assist with drug development and scientific research.

A comparison between ChatGPT, DeepSeek, and Manus highlighted their respective strengths and limitations:

  • ChatGPT: Developed in the US, resource-intensive, available in free and premium versions, and subject to strict content moderation.

  • DeepSeek: A Chinese model, efficient in coding and mathematics, open-source but with usage limits.

  • Manus: Also from China, lightweight and suitable for lower-resource environments, with applications in scheduling, web creation, and analysis.

The session addressed important ethical considerations:

  • Fairness: Ensuring training data is representative to avoid bias.

  • Transparency: Disclosing the use and training methods of AI tools to patients.

  • Data Privacy and Security: Ensuring personal data is securely managed.

  • Accountability: Clinicians remain responsible for decisions influenced by AI.

  • Informed Consent: Patients must be aware if AI tools are involved in their care.

  • Over-Reliance: Avoiding dependence on AI at the expense of clinical judgment.

  • Black Box Concern: Understanding how AI reaches its conclusions is critical.

The session concluded with a reminder: while AI is a powerful tool, it must be used responsibly and with proper validation. A key takeaway is the need for healthcare professionals to remain informed, critical, and accountable when integrating AI into practice.

Assignment: Choose an AI tool relevant to your specialty and submit a 5-minute recorded presentation outlining how ethical considerations are or can be addressed in its use.

Thank you, and see you in the next session.

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