The emergence of Generative AI

The advent of Generative AI in late 2022 marked a turning point for numerous industries, sparking a race to utilise this cutting-edge technology. Yet, in the healthcare industry, where the stakes are immeasurably high, the integration of Generative AI presents a complex challenge. The industry stands on the brink of unlocking a colossal $1 trillion in unrealized potential, but this must be navigated while meticulously adhering to stringent regulations like GDPR and HIPAA.

Understanding Generative AI and HIPAA compliance

Generative AI, offers unprecedented capabilities in data processing and decision-making. However, in healthcare, where patient data is sacrosanct, HIPAA (Health Insurance Portability and Accountability Act) compliance becomes paramount. This act, designed to safeguard patient privacy and data security, poses a significant hurdle for AI integration, demanding a delicate balance between innovation and compliance.

A recent press release from the HIPAA Journal brought to light a crucial concern: ChatGPT, despite its impressive capabilities, falls short of HIPAA compliance. It cannot, be employed for tasks involving patient notes or any form of Protected Health Information (PHI). This steers the conversation towards a critical question: how can healthcare organizations adopt Generative AI without breaching HIPAA guidelines?

The path to safe adoption: deploying AI securely

The key to safely using Generative AI lies in its deployment within a secure environment. Whether it's an on-premise data center or a Virtual Private Cloud (VPC), the principle remains the same: data security is paramount. Trusting these environments for data storage implicitly extends to deploying Generative AI applications, ensuring data never leaves the confines of this secured space.

Choosing the right AI model: open source vs. enterprise solutions

The landscape of Generative AI deployment is varied, with each approach bearing its own set of advantages and challenges. On one hand, open-source language models like llama and mistral offer healthcare institutions the flexibility to tailor high-performance language models within their secure environments, using their data. This approach promises total privacy, cost-effectiveness, and scalability, albeit with the requirement of significant GPU resources and technical expertise.

Conversely, enterprise versions of models like GPT-4 come with HIPAA-compliant enterprise contracts, allowing deployment within one's environment. While these models offer ease of construction, they come with limitations like potential vendor and model lock-in, less control over model training, and higher costs.

From our experience, using open-source models and deploying them in the institutions’ secure environment appears to be the most popular approach to adopting generative AI in a HIPAA compliant environment. Our clients are working with TitanML and using the Titan Takeoff Inference Server to make this process of deploying open-source Generative AI in their own environment significantly easier, all while requiring less access to GPUs. 

Reach out to [email protected] if you would like to learn more and find out if the Titan Takeoff Inference Server is right for your Generative AI application.