AI Considerations for General Practitioners
About the course
Target group
General Practitioners
Key words
Course introduction
The course, “AI Considerations for General Practitioners,” is structured into three modules, the first two featuring three lessons of approximately 15 minutes and the third featuring two lessons of approximately 15 minutes, blending video teaching and interactive content. The first module introduces AI in healthcare, covering foundational technologies, current applications and regulatory and ethical considerations. The second module delves into practical AI applications in general practice, including diagnostic support, administrative efficiency and personalised medicine. Finally, the third module addresses challenges in AI adoption, exploring trust and economic and operational requirements to integrate AI into practice. Designed for accessibility and practicality, this course equips GPs with the knowledge to responsibly and effectively incorporate AI into patient care.
Details to know
Downloadable certificate
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Assessment
8 Quizzes
Learning outcomes
Module 1
- Competence
- Is able to explain the fundamentals of artificial intelligence, such as machine learning, natural language processing, and robotics, and assess their relevance to health and care.
- Is able to assess the current state and future potential of AI in health and care, including its role in diagnostics, administrative efficiency, and patient engagement.
- Is able to identify and address regulatory and ethical considerations surrounding AI, such as data privacy, algorithmic bias, patient safety, and legal liabilities.
- Knowledge
- Knows the core processes of AI, including learning, reasoning, self-correction, and decision-making.
- Understands historical developments and current AI applications in health and care.
- Knows the future potential of AI in health and care, including predictive diagnostics and virtual health assistants.
- Understands the ethical dilemmas associated with AI in medical decision-making.
- Knows the legal and liability considerations of AI in health and care.
- Skills
- Differentiates between Narrow AI and General AI in health and care.
- Identifies key AI technologies such as machine learning, deep learning, NLP, and robotics.
- Evaluates how AI improves diagnostics, patient monitoring, and administrative workflows.
- Explores emerging AI technologies and evaluates their applicability in patient care.
- Implements best practices for safeguarding patient data under GDPR and other regulations.
- Identifies and mitigates risks related to algorithmic bias and transparency.
- Evaluates strategies to maintain patient trust and clinician oversight in AI-driven systems.
- Assesses the role of AI in patient safety and understand how to balance AI’s capabilities with human clinical oversight.
- Ensures compliance with evolving health and care AI regulations.
Module 2
- Competence
- Is able to evaluate practical applications of AI in general practice, including its use in diagnostic tools, clinical decision support systems, and the management of electronic health records.
- Is able to optimise general practice operations using AI-driven tools and automation.
- Isable to implement AI-driven personalised and precision medicine by using patient-specific data to tailor treatment plans and improve care outcomes.
- Knowledge
- Knows the differences between knowledge-based and non-knowledge-based clinical decision support systems (CDSS).
- Understands the integration of AI tools in patient diagnosis.
- Knows how AI streamlines administrative tasks, including scheduling and electronic health record (EHR) management.
- Understands how AI enhances patient interactions and clinic operations.
- Knows how AI analyses genetic, lifestyle, and medical history data for personalised treatments.
- Understands the limitations and challenges of AI in personalised medicine.
- Skills
- Identifies when to use knowledge-based vs. non-knowledge-based CDSS.
- Recognises the benefits of knowledge-based CDSS in following clinical guidelines.
- Explores how non-knowledge-based CDSS detect patterns in complex cases.
- Implements AI in diagnostic workflows to enhance clinician decision-making and improve diagnostic accuracy.
- Evaluates the reliability of AI-assisted diagnostics in fields such as radiology and dermatology.Uses AI-driven scheduling systems to improve workflow, optimise capacity and reduce no-show rates.
- Implements AI-based data entry and documentation tools to enhance accuracy.
- Recognises how AI can personalise patient interactions, offering tailored scheduling options, reminders, and post-care follow-ups to enhance patient satisfaction.
- Uses voice-driven recognition tools for efficient documentation, ensuring precise data entry directly within EHRs.
- Identifies how AI can support compliance with health regulations by standardising data entry and documentation processes.
- Assesses how AI-driven administrative processes contribute to a more patient-centred approach in general practice, fostering trust and improving care outcomes.
- Identifies AI-driven precision medicine approaches based on molecular and genetic information, including targeted treatment recommendations.
- Assesses AI’s ability to generate patient-specific treatment recommendations based on real-time patient data.
- Addresses concerns related to patient privacy, bias, and transparency in AI-driven medical recommendations.
- Ensures AI-driven interventions align with human clinical expertise and patient needs.
Module 3
- Competence
- Is able to address concerns about AI adoption in general practice by ensuring transparency, accountability, and patient trust.
- Is able to critically evaluate AI recommendations, ensuring they align with clinical judgment and maintain the physician’s role as the ultimate decision-maker.
- Is able to foster collaboration and trust in AI systems by ensuring transparency, accountability, and open communication with patients and developers, enhancing the integration of AI into general practice.
- Knowledge
- Knows common concerns regarding AI use in health and care, including existential anxiety, data misuse, and diagnostic bias.
- Understands the principles of transparency, accountability, and collaboration in AI systems.
- Knows the minimum requirements for adopting AI systems, such as ensuring time efficiency, diagnostic quality, data security, economic viability, transparency, and autonomy.
- Knows the principles of ensuring clinical autonomy in AI-assisted care
- Skills
- Identifies key concerns about AI adoption and evaluates their impact on patient trust and care.
- Develops strategies to mitigate fears and misconceptions about AI among health and care providers and patients.
- Discusses the role of GPs in ensuring patient data privacy and addressing concerns about data misuse.
- Explains how AI can enhance the physician-patient relationship by reducing administrative burdens and enabling meaningful interactions.
- Implements strategies to promote accountability in AI-driven medical decision support.
- Proposes strategies to identify and reduce algorithmic bias in AI-assisted diagnostics.
- Evaluates AI efficiency and diagnostic quality using performance indicators.
- Assesses economic and operational factors influencing AI implementation in health and care.
- Ensures AI adoption supports, rather than overrides, clinical judgment.
- Advocates for transparency and explainability in AI-driven decision support tools.
More detailed Learning Outcomes can be found in module introductions.
Introduction to AI in Healthcare
Module 1 of the “AI Considerations for General Practitioners programme” introduces AI’s role in healthcare, covering its applications, ethical challenges and regulatory aspects. It equips GPs with foundational knowledge to integrate AI safely and effectively, emphasising clinical benefits, data privacy, algorithmic bias and legal considerations.
Lessons
Introduction 1. Understanding AI and Its Applications 2. Current State and Future Potential of AI in Healthcare 3. Regulatory and Ethical Considerations ReferencesPractical AI Applications in General Practice
Module 2. This module explores AI’s role in improving diagnostics, streamlining efficiency, and enabling personalised care through three lessons on clinical decision-making, automating administrative tasks and precision medicine, emphasising ethical considerations like data privacy and algorithmic bias.
Lessons
Introduction 1. AI for Diagnostic Support and Decision-Making 2. Enhancing Efficiency in General Practice with AI 3. Personalised Medicine and AI ReferencesChallenges and Adoption of AI in General Practice
Module 3. This module explores integrating AI into general practice, addressing concerns like data misuse and bias and defining adoption standards. It emphasises transparency, accountability and balancing AI benefits with maintaining trust, care quality and GP autonomy.
Lessons
Introduction 1. Addressing Concerns and Building Trust in AI 2. Minimum Requirements for AI Adoption in GP Care References Course evaluation
Co-funded by the Erasmus+ programme of the European Union under Grant Agreement number 101056563.
Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EACEA. Neither the European Union nor the granting authority can be held responsible for them.