Alexey Litvin | CEO
Trusted by:
Why using ChatGPT might not be safe for Healthcare providers
Patient data privacy
Sending sensitive healthcare data to public AI models can lead to privacy breaches, exposing patient information and risking non-compliance with regulations like HIPAA.
Lack of control over data
Healthcare providers may lose control over data management with public AI models, leading to potential unauthorized access or misuse of patient information.
Potential for data leaks
Public AI platforms are vulnerable to cyberattacks, increasing the risk of significant data leaks and compromising sensitive healthcare information.
Unpredictable model behavior
Public AI models can change unpredictably due to various contributors, leading to inconsistent and unreliable responses that can impact patient care.
Why using ChatGPT might not be safe for Healthcare providers
Patient data privacy
Sending sensitive healthcare data to public AI models can lead to privacy breaches, exposing patient information and risking non-compliance with regulations like HIPAA.
Lack of control over data
Healthcare providers may lose control over data management with public AI models, leading to potential unauthorized access or misuse of patient information.
Potential for data leaks
Public AI platforms are vulnerable to cyberattacks, increasing the risk of significant data leaks and compromising sensitive healthcare information.
Unpredictable model behavior
Public AI models can change unpredictably due to various contributors, leading to inconsistent and unreliable responses that can impact patient care.
GreenM can install AI locally to secure your Healthcare data
How does it work?
Taking Large Language Model like ChatGPT
Installing it on a local server
Integrating it with your system
Fine-tuning the model using your dataset
result:
Data doesn’t leave your infrastructure, therefore it’s safe
GreenM can install AI locally to secure your Healthcare data
How does it work?
Taking Large Language Model like ChatGPT
Installing it on a local server
Integrating it with your system
Fine-tuning the model using your dataset
result:
Data doesn’t leave your infrastructure, therefore it’s safe
Testimonials
You can use your local AI model for
Patient Portals and Engagement Tools
to enhance patient portals with conversational interfaces for scheduling and FAQs.
Healthcare Analytics and Business Intelligence Tools
to mine data for predictive analytics and trend analysis.
Educational and Training Platforms
to provide virtual patient simulations and interactive learning modules.
Telehealth and Telemedicine Platforms
to facilitate virtual consultations and remote monitoring analysis.
Clinical Decision Support Systems (CDSS)
to provide real-time treatment recommendations and drug interaction alerts.
streamlining internal operations and technological infrastructure
to maximize efficiency and reduce costs.
You can use your local AI model for
Patient Portals and Engagement Tools
to enhance patient portals with conversational interfaces for scheduling and FAQs.
Healthcare Analytics and Business Intelligence Tools
to mine data for predictive analytics and trend analysis.
Educational and Training Platforms
to provide virtual patient simulations and interactive learning modules.
Telehealth and Telemedicine Platforms
to facilitate virtual consultations and remote monitoring analysis.
Clinical Decision Support Systems (CDSS)
to provide real-time treatment recommendations and drug interaction alerts.
streamlining internal operations and technological infrastructure
to maximize efficiency and reduce costs.
to find more try our AI assistant
For example:
Can I safely use LLM to analyze the quality of my customer reviews?
Our case study
Deploying a local LLM aka ChatGPT for the enhanced workflow
SHOW DETAILS
MAIN CHALLENGES
Data privacy concerns
High costs for cloud LLM
Lack of quality responses
Right dataset preparation
WHAT WE DID
Deployed a private LLM Llama-3 using Python and PyTorch.
Enhanced AI sentiment analysis tool for better output quality.
Ensured data privacy by keeping processing local.
Prepared and integrated dataset with bad and good responses for fine-tuning.
RESULTS
Improved Response Quality: Achieved a quality score of 382 for Llama-3:8b tuned.
Cost Efficiency: Reduced processing cost to $0.005 per 1,000 tokens.
Enhanced Performance: Configured 8 billion parameters for complex responses.
GreenM: Leading Healthcare innovation with generative AI expertise
AWS certified
AWS certified
Azure OpenAI
AI House
Global AI community
Meta AI
Years experience in Healthcare domain
Department focused on AI research
Team
Anton
Avramenko
Lead of AI Research
Vitalii
Samarskyi
Solution Architect
Lena
Kirienko
Head of Product
Alexey
Litvin
CEO, Founder
GreenM: Leading Healthcare innovation with generative AI expertise
AWS certified
AWS certified
Azure OpenAI
AI House
Global AI community
Meta AI
Years experience in Healthcare domain
Department focused on AI research
AWS certified
AWS certified
Azure OpenAI
AI House
Global AI community
Meta AI
Years experience in Healthcare domain
Department focused on AI research
Our Team
Anton
Avramenko
Lead of AI Research
Vitalii
Samarskyi
Solution Architect
Lena
Kirienko
Head of Product
Alexey
Litvin
CEO, Founder