AI+ Medical Assistant™

Kód kurzu: AP5010

Tato část není lokalizována

Revolutionize Healthcare Support with AI-Powered Medical Assistance

Patient Interaction Excellence: Learn how AI enhances patient communication, appointment scheduling, and follow-up care to improve the patient experience.
Clinical Workflow Efficiency: Master AI tools for streamlining patient intake, medical record management, and lab result analysis to optimize clinical operations.
Data-Driven Decision Support: Gain expertise in using AI to assist healthcare providers with accurate diagnostics, treatment suggestions, and patient monitoring.
Enhanced Medical Administration: Prepare to support healthcare teams with AI-driven administrative tasks, reducing errors, improving accuracy, and enabling faster decision-making.

Price of the certification exam is included in the price of the course.

4 300 Kč

5 203 Kč s DPH

Výběr termínů

Máte dotaz?

+420 731 175 867 edu@edutrainings.cz

Odborní
certifikovaní lektoři

Mezinárodně
uznávané certifikace

Široká nabídka technických
a soft skills kurzů

Zobrazit soft skills kurzy

Skvělý zákaznický
servis

Přizpůsobení kurzů
přesně na míru

Termíny kurzu
Popis kurzu
Cílová skupina
Struktura kurzu
Předpokládané znalosti
Certifikace

Termíny kurzu

Počáteční datum: Na vyžádání

Forma: Self-paced

Délka kurzu: 8 hodin

Jazyk: en

Cena bez DPH: 4 300 Kč

Registrovat

	Počáteční datum	Místo konání	Forma	Délka kurzu	Jazyk	Cena bez DPH
	Na vyžádání		Self-paced	8 hodin	en	4 300 Kč	Registrovat
G	Garantovaný kurz

Nenašli jste vhodný termín?

Napište nám o vypsání alternativního termínu na míru.

Kontakt

Popis kurzu

Tato část není lokalizována

Increased Demand for AI Skills:

Healthcare organizations are adopting AI, increasing the need for skilled administrators to manage these systems.

Improved Efficiency and Cost Reduction:

AI streamlines tasks, reducing costs and boosting efficiency, making AI expertise vital for healthcare management.

Enhanced Decision-Making:

AI-driven data analysis supports better resource planning and informed decisions, improving healthcare outcomes.

Compliance and Risk Management:

AI tools help administrators ensure regulatory compliance, privacy, and risk management in healthcare organizations.

Career Growth Opportunities:

The certification opens doors to leadership roles, allowing you to drive digital transformation and enhance operations.

TensorFlow
Keras
Python
Natural Language Processing (NLP) Tools
SQL
Matplotlib
Power BI
Healthcare Data Integration Tools
Electronic Health Record (EHR) Systems
Patient Scheduling and Coordination Platforms
AI-Powered Diagnostic Tools
Medical Imaging Analysis Tools

Cílová skupina

Tato část není lokalizována

Healthcare Support Professionals: Individuals looking to enhance their skills with AI tools to streamline patient care and improve clinical support.

Medical Office Administrators: Professionals interested in using AI to automate administrative tasks, optimize scheduling, and enhance patient coordination.

Clinical Staff Members: Nurses, medical assistants, and technicians aiming to integrate AI into their daily workflows for improved efficiency and patient care.

Aspiring Medical Technologists: Those seeking to work with AI-driven medical tools and enhance diagnostic capabilities and patient monitoring.

Healthcare Technology Enthusiasts: Individuals passionate about merging healthcare knowledge with AI innovations to drive digital transformation in medical settings.

Struktura kurzu

Tato část není lokalizována

Module 1: Fundamentals of AI for Medical Assistants

1.1 Understanding AI and Its Healthcare Applications
1.2 The Role of AI in Medical Assistance
1.3 Case Studies
1.4 Hands-on Session: Functionality Survey and Stepwise Analysis of the Eka.care Patient-Side Application

Module 2: Data Literacy for Medical Assistants

2.1 Healthcare Data Types and Management
2.2 Using Data Effectively in AI
2.3 Case Studies
2.4 Hands-On Session: Structured vs. Unstructured Data in Healthcare: A Practical Study Using Eka.Care Patient Health Record System

Module 3: AI in Patient Care Optimization

3.1 Enhancing Patient Interactions with AI
3.2 Predictive Analytics and Workflow Management
3.3 Case Studies
3.4 Hands-On Session: Eka.care in Action: Appointment Management, Smart Reminders & Tele-Consult Dashboards

Module 4: NLP and Generative AI in Medical Documentation

4.1 Foundations of NLP for Medical Assistants
4.2 Practical Applications and Risks
4.3 Case Studies
4.4 Hands-On Simulation Exercise
4.5 Hands-On Session: Automating Clinical Documentation Using Eka.care: Notes, Summaries, and Communication Workflows

Module 5: AI in Diagnostics and Screening

5.1 Diagnostic Support Tools
5.2 Real-World Applications and Simulation
5.3 Use Cases
5.4 Hands-On: AI-Powered Detection of Common Health Conditions: Review and Analysis of AI-Suggested Diagnostic Insights using Eka Care

Module 6: Ethics, Bias, and Regulation in AI for Healthcare

6.1 Recognizing and Addressing Bias in AI
6.2 Legal, Ethical, and Compliance Frameworks
6.3 Hands-On Exercise: Analyzing and Visualizing Bias in Artificial Intelligence Systems — Exploring Racial, Socioeconomic, and Demographic Disparities using Google’s What-If Tool

Module 7: Evaluating and Implementing AI Tools

7.1 Selecting and Planning for AI Adoption
7.2 Best Practices and Stakeholder Engagement
7.3 Case Study: Procurement and Early Deployment of AI Tools for Chest Diagnostics in a National Health Service Setting
7.4 Hands-On Simulation Exercise: Recognizing Red Flags in Vendor Solutions for AI in Medical Assistant
7.5 Hands-On Exercises: Evaluating the Relevance and Effectiveness of AI Models using the Zoho Analytics

Module 8: Cybersecurity and Emerging Trends in AI

8.1 Cybersecurity Risks and Protection
8.2 Future Trends and Preparing for Innovation
8.3 Case Studies: EY’s Strategic Transformation: Adapting to Emerging AI Technologies
8.4 Hands-On Exercises: Common Cybersecurity Threats in AI-Enabled Healthcare: A Hands-On Exploration Using Google Sheets

Předpokládané znalosti

Tato část není lokalizována

A basic understanding of medical terminology, foundational AI and machine-learning concepts, data analytics skills for interpreting medical data, proficiency in programming languages like Python, and knowledge of healthcare systems and clinical workflows are essential for this course.