AI for Healthcare Training in Austin

This instructor-led, live training in Austin (online or onsite) is aimed at intermediate to advanced medical AI developers and data scientists who wish 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 transfer learning, domain adaptation, and model compression techniques 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 enhance patient care and outcomes.

Generative AI is a technology designed to create new content, including 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 leverage generative AI and prompt engineering to enhance efficiency, accuracy, and communication in medical contexts for government use.

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

• Understand the foundational principles of generative AI and prompt engineering.
• Apply AI tools to streamline clinical, administrative, and research tasks in a healthcare setting.
• Ensure ethical, safe, and compliant use of AI within the healthcare sector.
• 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

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

This instructor-led, live training in Austin (online or onsite) is designed for intermediate-level data scientists and healthcare professionals who aim to leverage 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 applications using Google Colab.
• Utilize AI for predictive modeling in healthcare data.
• Analyze medical images with AI-driven techniques.
• Examine ethical considerations in AI-based healthcare solutions.

This training is tailored to enhance the capabilities of professionals for government and private sector applications, ensuring alignment with public sector workflows, governance, and accountability.

This instructor-led, live training in Austin (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 fundamental principles of ChatGPT and its applications in healthcare settings for government.
• Utilize ChatGPT to automate healthcare processes and interactions, enhancing efficiency and accuracy.
• Provide accurate medical information and support to patients through the use of ChatGPT.
• Apply ChatGPT for medical research and data analysis, supporting evidence-based decision-making.

This instructor-led, live training in Austin (online or onsite) is designed for government healthcare professionals, data analysts, and policy makers at the beginner to intermediate level who seek to understand and apply generative AI within 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 for government use.
• Identify opportunities for generative AI to enhance drug discovery and personalized medicine within public sector workflows.
• Utilize generative AI techniques for medical imaging and diagnostics, aligning with governance standards.
• Assess the ethical implications of AI in medical settings, ensuring accountability and transparency.
• Develop strategies for integrating AI technologies into healthcare systems, supporting public sector objectives.

This instructor-led, live training in Austin (online or onsite) is designed for intermediate to advanced healthcare professionals and artificial intelligence developers who aim to implement AI-driven 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 for government.
• Ensure compliance with healthcare regulations and ethical AI practices.

This instructor-led, live training in Austin (online or onsite) is designed for intermediate-level healthcare professionals and data scientists who seek to understand and implement AI technologies within healthcare settings.

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

• Identify key healthcare challenges that can be addressed through AI solutions.
• Analyze the impact of AI on patient care, safety, and medical research for government and private sector applications.
• Understand the relationship between AI and healthcare business models for government initiatives.
• Apply fundamental AI concepts to healthcare scenarios for improved decision-making and operational efficiency.
• Develop machine learning models for medical data analysis, enhancing public health outcomes for government programs.

Agentic AI is an approach where artificial intelligence systems are designed to plan, reason, and take tool-using actions to achieve specific goals within defined parameters.

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 in the public sector.

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

• Explain agentic AI concepts and constraints within 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 with guardrails and human-in-the-loop controls to ensure compliance and safety for government applications.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Austin (online or onsite) is designed for intermediate to advanced healthcare professionals, medical researchers, and AI developers who seek to leverage multimodal AI in medical diagnostics and healthcare applications.

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

• Understand the role of multimodal AI in contemporary healthcare for government and private sector settings.
• Integrate structured and unstructured medical data to enhance AI-driven diagnostics for government and clinical environments.
• Apply AI techniques to analyze medical images and electronic health records, improving patient care and outcomes.
• Develop predictive models for disease diagnosis and treatment recommendations, supporting evidence-based decision-making.
• Implement speech and natural language processing (NLP) technologies for accurate medical transcription and improved patient interaction.

This instructor-led, live training in Austin (online or onsite) is aimed at intermediate-level healthcare professionals, biomedical engineers, and artificial intelligence developers who wish to leverage Edge AI for innovative healthcare solutions for government.

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

• Understand the role and benefits of Edge AI in healthcare for government.
• Develop and deploy AI models on edge devices for healthcare applications within public sector environments.
• Implement Edge AI solutions in wearable devices and diagnostic tools to enhance public health initiatives.
• Design and deploy patient monitoring systems using Edge AI to improve healthcare delivery for government.
• Address ethical and regulatory considerations specific to healthcare AI applications within the public sector.

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 the development of decision-support systems that align with medical workflows for government.

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges for government.

By the end 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 for government.
• Deploy, monitor, and validate LangGraph applications in healthcare production settings for government.

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 Austin (online or onsite) is designed for intermediate-level healthcare professionals and artificial intelligence developers who seek to enhance medical workflows, research efficiency, and patient outcomes through the application of prompt engineering techniques.

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

• Comprehend the foundational principles of prompt engineering in healthcare settings.
• Utilize AI prompts for clinical documentation and patient interactions to improve accuracy and efficiency.
• Apply AI-driven methods for medical research and literature review to accelerate discovery and knowledge dissemination.
• Enhance drug discovery and clinical decision-making processes through the strategic use of AI-generated prompts.
• Ensure adherence to regulatory and ethical standards in the deployment of healthcare AI, maintaining compliance for government and public sector requirements.

TinyML is the integration of machine learning into low-power, resource-limited wearable and medical devices.

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 for government.

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 discussions.
• 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, please contact us to customize the program for government.

This instructor-led, live training in Austin (online or onsite) is designed for intermediate-level healthcare professionals who wish to integrate artificial intelligence (AI) and augmented reality/virtual reality (AR/VR) solutions for government medical training, surgery simulations, and rehabilitation.

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

• Understand the role of AI in enhancing AR/VR experiences within healthcare settings.
• Utilize AR/VR technologies for surgery simulations and medical education.
• Implement AR/VR tools in patient rehabilitation and therapeutic interventions.
• Examine the ethical and privacy implications of AI-enhanced medical applications.