AI in Healthcare Training Course

Agentic AI represents an approach where AI systems plan, reason, and take tool-using actions to accomplish goals within defined constraints.

This instructor-led, live training (online or onsite) is aimed at intermediate-level healthcare and data teams who wish to design, evaluate, and govern agentic AI solutions for clinical and operational use cases.

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

• Explain agentic AI concepts and constraints in healthcare contexts.
• Design safe agent workflows with planning, memory, and tool usage.
• Build retrieval-augmented agents over clinical documents and knowledge bases.
• Evaluate, monitor, and govern agent behavior with guardrails and human-in-the-loop controls.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises on safety, evaluation, and governance.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in India (online or on-site) is designed for intermediate to advanced healthcare professionals and AI developers who want to implement AI-driven healthcare solutions.

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

• Grasp the role of AI agents in healthcare and diagnostics.
• Create AI models for medical image analysis and predictive diagnostics.
• Integrate AI with electronic health records (EHR) and clinical workflows.
• Ensure adherence to healthcare regulations and ethical AI practices.

This instructor-led, live training in India (online or onsite) is designed for intermediate-level healthcare professionals who want to apply AI and AR/VR solutions for medical education, surgical simulations, and rehabilitation.

Upon completion of this training, participants will be able to:

• Comprehend how AI improves AR/VR experiences within healthcare.
• Utilise AR/VR for surgical simulations and medical education.
• Implement AR/VR tools for patient rehabilitation and therapy.
• Investigate the ethical and privacy issues surrounding AI-enhanced medical instruments.

This instructor-led live training (online or onsite) is designed for intermediate-level data scientists and healthcare professionals aiming to utilize AI for advanced healthcare applications through Google Colab.

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

• Implement AI models for healthcare using Google Colab.
• Use AI for predictive modeling in healthcare data.
• Analyze medical images with AI-driven techniques.
• Explore ethical considerations in AI-based healthcare solutions.

This instructor-led, live training in India (online or onsite) is designed for healthcare professionals and researchers who wish to leverage ChatGPT to enhance patient care, streamline workflows, and improve healthcare outcomes.

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

• Understand the fundamentals of ChatGPT and its applications in healthcare.
• Utilize ChatGPT to automate healthcare processes and interactions.
• Provide accurate medical information and support to patients using ChatGPT.
• Apply ChatGPT for medical research and analysis.

This live, instructor-led training in India (online or onsite) targets intermediate-level healthcare professionals, biomedical engineers, and AI developers who want to leverage Edge AI for innovative healthcare solutions.

Upon completing this training, participants will be able to:

• Grasp the role and advantages of Edge AI in the healthcare sector.
• Build and deploy AI models on edge devices for healthcare use cases.
• Implement Edge AI solutions in wearable devices and diagnostic tools.
• Design and deploy patient monitoring systems leveraging Edge AI.
• Navigate ethical and regulatory considerations in healthcare AI applications.

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

Upon completing this training, participants will be equipped to:

• Refine AI models using healthcare datasets, including EMRs, imaging, and time-series data.
• Implement transfer learning, domain adaptation, and model compression within medical contexts.
• Manage privacy, bias, and regulatory compliance during model development.
• Deploy and monitor refined models in practical healthcare settings.

Generative AI is a technology that creates new content such as text, images, and recommendations based on prompts and data.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level healthcare professionals who wish to use generative AI and prompt engineering to improve efficiency, accuracy, and communication in medical contexts.

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

• Grasp the core concepts of generative AI and prompt engineering.
• Utilise AI tools to streamline clinical, administrative, and research tasks.
• Ensure ethical, safe, and compliant use of AI in healthcare.
• Refine prompts to achieve consistent and accurate results.

Format of the Course

• Interactive lecture and discussion.
• Practical exercises and case studies.
• Hands-on experimentation with AI tools.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in India (online or onsite) is designed for beginner to intermediate-level healthcare professionals, data analysts, and policymakers who wish to understand and apply generative AI within the healthcare context.

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 personalised medicine.
• Utilise 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.

LangGraph facilitates stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these features are vital for ensuring compliance, enabling interoperability, and developing decision-support systems that align with medical workflows.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

Upon completion of this training, participants will be able to:

• Design healthcare-specific LangGraph workflows with a focus on compliance and auditability.
• 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 India (online or onsite) is designed for intermediate to advanced-level healthcare professionals, medical researchers, and AI developers looking to apply multimodal AI in medical diagnostics and healthcare solutions.

Upon completing this training, participants will be able to:

• Grasp the role of multimodal AI in contemporary healthcare.
• Integrate structured and unstructured medical data for AI-powered diagnostics.
• Apply AI techniques to analyse medical images and electronic health records.
• Build predictive models for disease diagnosis and treatment suggestions.
• Implement speech and natural language processing (NLP) for medical transcription and patient engagement.

Ollama is a lightweight platform designed for running large language models locally.

This instructor-led, live training (available online or onsite) is tailored for intermediate-level healthcare practitioners and IT teams seeking to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative settings.

Upon completing this training, participants will be able to:

• Install and configure Ollama to ensure secure usage in healthcare environments.
• 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 customized training for this course, please contact us to arrange.

This instructor-led, live training in India (online or onsite) is designed for intermediate-level healthcare professionals and AI developers who wish to leverage prompt engineering techniques to improve medical workflows, research efficiency, and patient outcomes.

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

• Understand the fundamentals of prompt engineering in healthcare.
• Use AI prompts for clinical documentation and patient interactions.
• Leverage AI for medical research and literature review.
• Enhance drug discovery and clinical decision-making with AI-driven prompts.
• Ensure compliance with regulatory and ethical standards in healthcare AI.

TinyML refers to the integration of machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led training session, available both online and onsite, is designed for intermediate-level professionals aiming to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completion of this course, participants will be equipped to:

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
• Optimize models specifically for low-power and memory-constrained wearable devices.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures supplemented with live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Guided implementation exercises within a lab environment.

Customization Options

• For training tailored to specific healthcare devices or regulatory workflows, please contact us to customize the program.