What is AI for Blood Donor Recruitment?
Introduction to AI
In recent years, the field of artificial intelligence (AI) has made significant strides, particularly in the when it comes to language processing and generative AI. From virtual assistants like Siri and Alexa to sophisticated chatbots providing customer service and now Incept Health’s Betty Blood™, AI's capability to interact with humans through natural language has revolutionized the way we communicate with blood donors.
In this blog, we will explore the definitions of these essential AI concepts, dig into how LLMs acquire their capabilities, and discuss the critical data privacy considerations that accompany their use. By understanding these aspects, you can better navigate the complex landscape of AI and leverage its potential while being protective of your blood center’s data.
Did you catch our other AI blog posts?
How Blood Centers Can Use AI to Predict Blood Shortages and Optimize Recruitment Outreach
10 Key Tips Blood Centers Need to Know Before Using AI to Recruit Blood Donors
Definition of Key Terms
Natural Language Processing (NLP)
NLP is the field of study focused on the interaction between humans and computers through natural language. NLP bridges the gap between human communication and computer understanding, enabling computers to process and analyze large amounts of natural language data.
Large Language Model (LLM)
A Large Language Model (LLM) is a type of AI model designed to understand and generate human-like text based on large datasets. One prominent example of an LLM is OpenAI's ChatGPT. LLMs can be general-purpose, like ChatGPT, or they can be tailored to specific domains, such as healthcare patient recruitment or blood donor recruitment, where they are trained with specific data to those domains
Generative AI
Generative AI refers to algorithms that can create new content, whether it's text, images, or audio, from scratch. These models use patterns and information from their training data to generate outputs that resemble human-created content.
Conversational AI
Conversational AI refers to AI that enables computers to understand and respond to human language in a natural and meaningful way. This includes chatbots and virtual assistants, which can carry on conversations with users, answer questions, provide recommendations, and perform tasks.
How Does an LLM “Learn”?
LLMs learn by analyzing vast amounts of text data available to them during training. For instance, ChatGPT is trained primarily using publicly available information on the internet.
Domain-specific LLMs, on the other hand, are trained with data tailored to a particular field. For example, an LLM designed for a blood donor recruitment virtual assistant, like Betty Blood, is trained with conversation data specific to that scenario. This specialized training helps the model perform better in its designated domain by understanding context-specific terminology and interactions.
One important concept in the learning process of LLMs is "hallucinations." This term refers to instances where the model generates incorrect or nonsensical answers. Utilizing domain-specific LLMs or more controlled models can reduce the occurrence of hallucinations by ensuring the model has access to accurate and relevant data for its specific use case.
Data Privacy Considerations
When using general LLMs like OpenAI's ChatGPT, it’s crucial to consider data privacy implications. These models can consume and use any proprietary and sensitive data provided to them during interactions. This poses potential risks, such as exposure to data breaches or misuse of sensitive information.
To mitigate these risks, it is essential to evaluate the security measures and data handling policies of the LLM provider. For applications requiring stringent data privacy, opting for domain-specific LLMs that do not expose sensitive data to external servers can be a safer choice. Additionally, implementing robust data encryption and access control measures can further protect sensitive information from unauthorized access.
Betty Blood™ utilizes underlying LLM technology that is specific to blood donor recruitment, and instances of her are separated for each blood center that utilizes Betty – only general blood donor recruitment is fed to every instance of Betty Blood™, and blood center specific and proprietary information is only used for the defined instance of Betty.
Conclusion
Understanding the key terms and concepts that play a part in AI is fundamental for leveraging these technologies effectively. By comprehending how LLMs learn and being mindful of data privacy considerations, businesses can harness the power of these advanced models while safeguarding their sensitive information.
Want to learn more about adding conversational AI to your blood donor recruitment?
Learn more about Betty Blood™ here and contact us to see how it can help your recruitment goals!