ChatGPT for Healthcare Training Course

Agentic AI is an approach where artificial intelligence systems are designed to plan, reason, and utilize tools to achieve specific goals within established parameters. This instructor-led, live training (available online or on-site) is targeted at intermediate-level healthcare and data teams who aim to design, evaluate, and govern agentic AI solutions for both clinical and operational applications. The training aligns with the needs of public sector workflows, governance, and accountability for government agencies. By the end of this training, participants will be able to: - Explain the principles and constraints of agentic AI in healthcare contexts. - Design safe agent workflows that incorporate planning, memory, and tool usage. - Develop retrieval-augmented agents using clinical documents and knowledge bases. - Evaluate, monitor, and govern agent behavior through the use of guardrails and human-in-the-loop controls. **Format of the Course** - Interactive lectures and facilitated discussions. - Guided labs and code walkthroughs in a secure sandbox environment. - Scenario-based exercises focused on safety, evaluation, and governance. **Course Customization Options** - To request a customized training for government agencies or other organizations, please contact us to arrange.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate to advanced-level healthcare professionals and artificial intelligence developers who aim to implement AI-driven healthcare solutions for government. By the end of this training, participants will be able to: - Comprehend the role of AI agents in healthcare and diagnostics. - Develop AI models for medical image analysis and predictive diagnostics. - Integrate AI with electronic health records (EHR) and clinical workflows. - Ensure compliance with healthcare regulations and ethical AI practices.

This instructor-led, live training (online or onsite) is designed for intermediate-level healthcare professionals who aim to integrate artificial intelligence (AI) and augmented reality/virtual reality (AR/VR) solutions into medical training, surgery simulations, and rehabilitation programs. By the end of this training, participants will be able to: - Comprehend the role of AI in enhancing AR/VR experiences within the healthcare sector. - Utilize AR/VR technologies for conducting surgery simulations and medical training. - Implement AR/VR tools in patient rehabilitation and therapeutic interventions. - Examine the ethical and privacy considerations associated with AI-enhanced medical applications, ensuring compliance with standards for government and regulatory requirements.

Explore how artificial intelligence (AI) can revolutionize human resources processes—from recruitment to payroll and training—through practical, hands-on modules. This training equips HR professionals with the skills to implement AI ethically and effectively, enhancing efficiency, accuracy, and the employee experience.

Why Participate?

• Acquire practical AI skills specifically tailored for HR
• Master the automation and optimization of key HR tasks
• Gain a comprehensive understanding of ethical use and risk management
• Prepare your HR function to meet future challenges, ensuring alignment with public sector workflows and governance for government

This instructor-led, live training (conducted online or on-site) is designed for intermediate-level data scientists and healthcare professionals who aim to utilize artificial intelligence for advanced healthcare applications using Google Colab. By the end of this training, participants will be able to: - Implement AI models for healthcare using Google Colab. - Apply AI for predictive modeling in healthcare data. - Analyze medical images with AI-driven techniques. - Examine ethical considerations in AI-based healthcare solutions for government.

This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare professionals and data scientists who aim to understand and implement artificial intelligence (AI) technologies in healthcare settings. By the end of this training, participants will be able to: - Identify key challenges in healthcare that can be addressed through AI. - Analyze the impact of AI on patient care, safety, and medical research. - Understand the relationship between AI and healthcare business models. - Apply fundamental AI concepts to various healthcare scenarios. - Develop machine learning models for the analysis of medical data, enhancing decision-making processes for government and private sector applications.

This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare professionals, biomedical engineers, and artificial intelligence developers who aim to utilize Edge AI for innovative healthcare solutions. By the end of this training, participants will be able to: - Understand the role and benefits of Edge AI in healthcare. - Develop and deploy AI models on edge devices for healthcare applications. - Implement Edge AI solutions in wearable devices and diagnostic tools. - Design and deploy patient monitoring systems using Edge AI. - Address ethical and regulatory considerations in healthcare AI applications, ensuring alignment with public sector workflows, governance, and accountability for government.

This instructor-led, live training (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who aim to refine models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

By the end of this training, participants will be able to:

• Fine-tune AI models on healthcare datasets, including electronic medical records (EMRs), imaging, and time-series data.
• Apply techniques such as transfer learning, domain adaptation, and model compression in medical contexts.
• Address privacy concerns, bias mitigation, and regulatory compliance in the development of AI models for government and healthcare settings.
• Deploy and monitor fine-tuned models in real-world healthcare environments to ensure effective and ethical use.

Generative AI is a technology designed to create new content such as text, images, and recommendations based on user prompts and data. This instructor-led, live training (available online or onsite) is targeted at beginner to intermediate healthcare professionals who wish to leverage generative AI and prompt engineering to enhance efficiency, accuracy, and communication in medical settings. By the end of this training, participants will be able to: - Understand the foundational concepts of generative AI and prompt engineering. - Utilize AI tools to streamline clinical, administrative, and research tasks. - Ensure ethical, safe, and compliant use of AI in healthcare environments. - Optimize prompts to achieve consistent and accurate outcomes. **Format of the Course** - Interactive lectures and discussions. - Practical exercises and case studies. - Hands-on experimentation with AI tools. **Course Customization Options for Government** To request a customized training for this course tailored to specific needs, please contact us to arrange.

This instructor-led, live training (available online or onsite) is designed for government and healthcare professionals, data analysts, and policy makers at beginner to intermediate levels. The course aims to provide a comprehensive understanding of generative AI in the context of healthcare. By the end of this training, participants will be able to: - Explain the principles and applications of generative AI in healthcare. - Identify opportunities for generative AI to enhance drug discovery and personalized medicine. - Utilize generative AI techniques for medical imaging and diagnostics. - Assess the ethical implications of AI in medical settings. - Develop strategies for integrating AI technologies into healthcare systems, ensuring alignment with public sector workflows and governance.

LangGraph enables stateful, multi-actor workflows powered by LLMs with precise control over execution paths and state persistence. In healthcare, these capabilities are essential for compliance, interoperability, and building decision-support systems that align with medical workflows. This instructor-led, live training (online or onsite) is aimed at intermediate to advanced professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges for government and private sector applications. By the end of this training, participants will be able to: - Design healthcare-specific LangGraph workflows with compliance and auditability in mind. - Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD). - Apply best practices for reliability, traceability, and explainability in sensitive environments. - Deploy, monitor, and validate LangGraph applications in healthcare production settings. **Format of the Course** - Interactive lecture and discussion. - Hands-on exercises with real-world case studies. - Implementation practice in a live-lab environment. **Course Customization Options** - To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate to advanced healthcare professionals, medical researchers, and AI developers who seek to apply multimodal AI in medical diagnostics and healthcare applications for government. By the end of this training, participants will be able to: - Comprehend the role of multimodal AI in modern healthcare. - Integrate structured and unstructured medical data to enhance AI-driven diagnostics. - Utilize AI techniques to analyze medical images and electronic health records. - Construct predictive models for disease diagnosis and treatment recommendations. - Implement speech and natural language processing (NLP) technologies for medical transcription and patient interaction.

Ollama is a lightweight platform designed for running large language models locally. This instructor-led, live training (online or onsite) is aimed at intermediate-level healthcare practitioners and IT teams who wish to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative environments. Upon completing this training, participants will be able to: - Install and configure Ollama for secure use in healthcare settings. - Integrate local LLMs into clinical workflows and administrative processes. - Customize models for healthcare-specific terminology and tasks. - Apply best practices for privacy, security, and regulatory compliance. **Format of the Course** - Interactive lecture and discussion. - Hands-on demonstrations and guided exercises. - Practical implementation in a sandboxed healthcare simulation environment. **Course Customization Options** - To request a customized training for this course, please contact us to arrange. This option is available for government agencies and private organizations seeking tailored solutions for their specific needs.

This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare professionals and artificial intelligence developers who aim to utilize prompt engineering techniques to enhance medical workflows, research efficiency, and patient outcomes.

By the end of this training, participants will be able to:

• Comprehend the foundational principles of prompt engineering in the healthcare sector.
• Apply AI prompts for clinical documentation and patient interactions.
• Utilize AI tools for medical research and literature review.
• Improve drug discovery and clinical decision-making through AI-driven prompts.
• Ensure adherence to regulatory and ethical standards in healthcare AI applications, aligning with best practices for government and public sector workflows.

TinyML is the integration of machine learning into low-power, resource-limited wearable and medical devices for government and public sector applications. This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to implement TinyML solutions for healthcare monitoring and diagnostic applications. After completing this training, participants will be able to: - Design and deploy TinyML models for real-time health data processing. - Collect, preprocess, and interpret biosensor data for AI-driven insights. - Optimize models for low-power and memory-constrained wearable devices. - Evaluate the clinical relevance, reliability, and safety of TinyML-driven outputs. **Format of the Course** - Lectures supported by live demonstrations and interactive discussion. - Hands-on practice with wearable device data and TinyML frameworks. - Implementation exercises in a guided lab environment. **Course Customization Options** - For tailored training that aligns with specific healthcare devices or regulatory workflows for government, please contact us to customize the program.