Develop a Prototype
Identify use cases, define success KPIs, and create a proof of concept of your generative AI solution
With the explosion of generative AI and Large Language Models (LLMs) in recent years, healthcare organizations of all sizes are exploring how they can integrate these technologies into their products and solutions. And for good reason. Generative AI has enormous potential to automate tasks, enhance the patient experience, and streamline care processes.
As with any new technology, implementing it is more of a marathon than a sprint. Still, organizations are under pressure to produce live solutions quickly.
Cloudticity can help. With our AI for Healthcare Kickstarter, a 30-day workshop to proof of concept on AWS, our generative AI and healthcare cloud experts can help you realize your AI goals faster, with less pressure on your team. Schedule a free consultation today to learn more.
Identify use cases, define success KPIs, and create a proof of concept of your generative AI solution
Determine data quality and availability, define data requirements, and build data cleaning and processing pipelines
Implement data security solutions and best practices, such as encryption and identity and access management
Ensure your solution meets compliance requirements for HIPAA, HITRUST, NIST, FISMA, SOC 2, and more
Identify data sources and build data collection, cleaning, processing, and augmentation pipelines
Fine-tune pre-trained models and optimize prompts, deploy the model in batch or real-time mode and optimize for latency and cost
Introducing generative AI solutions and capabilities into your offerings not only solidifies your presence in the competitive landscape but also amplifies the value delivered to your customers. This cutting-edge technology can help you automate routine tasks, optimize care workflows, and improve patient experience.
One of the biggest roadblocks for generative AI adoption in healthcare is data requirements. In order for AI models to be effective, the data needs to be high quality and available. More often than not, this requires data preparation and data management pipelines.