Generative artificial intelligence (AI) has been the latest disruptor for many industries, quickly becoming widely used by companies that run the gamut. However, given the sensitive data and importance of privacy in the healthcare industry, its use there has driven more controversy than almost anywhere else.
Health providers are at the very least curious about the tools and exploring ways to safely integrate their functionalities into their operations. Conversely, many are already implementing the technology in various ways to accomplish those goals.
No matter what camp healthcare providers sit in, it’s important for them to understand how they can harness the power of responsible generative AI. Here we dive into some use cases to consider exploring and how it’s impacting healthcare – for the better.
Use case No. 1: Searching and managing medical records and assets
One use case of how providers can turn to generative AI is when they are searching and managing medical records. The tool can be used when developing descriptions and tags for assets and media thus making the process of looking up records and documenting patient information faster. It can even be used to summarize medical conversations from recorded audio during patient visits.
Use case No. 2: Interpreting unstructured data
Every organization has unstructured data. In healthcare, those data include medical notes, electronic health data, and images such as X-rays and MRIs. This type of data can create gaps during analysis, making conversation into structured data incredibly important.
Generative AI is able to identify unstructured data and convert it by analyzing data from multiple sources and giving providers comprehensive insight. For example, providers can leverage the tool to summarize a patient’s medical history, which likely includes data from several sources (primary care physician, cardio, neuro and more). Providers can also extend the value of the AI tool through the application of large multimodal models (LLMs). Since LLMs are most effective when trained by memorizing high volumes of data, the input of complex data, such as lab results and studies, will only increase their accuracy.
Use case No. 3: Improving disease diagnosis
While generative AI should not be considered a primary diagnosis tool, it can be a valuable tool for medical professionals during the diagnosis process. Many providers and systems are using it as their medical knowledge assistant to identify and diagnose different diseases. In those instances, generative AI can create a checklist of symptoms for diseases and provide options for potential treatment.
Additionally, LLMs with generative AI can be fine-tuned in medical datasets to help with early diagnosis by using available sensors such as phone cameras or health watches.
Academic medical centers have also been building LLMs to drive improved outcomes and feed innovation. Pairing the work completed by academic centers with new AI tool will accelerate change faster than expected.
Use case No. 4: Learning synthetic content
Finding well-managed and contextualized medical content to support learning and practicing can be difficult. However, generative AI can provide models that produce very realistic examination images that correspond with potential situations based on gender, age, organ system, race and other factors. Leveraging this synthetic content, students and medical doctors have more access to materials that allow them to better understand diagnosis and diseases. This is especially useful when overcoming gaps on a particular hospital dataset.
Use case No. 5: Providing personalized and improved care
Since generative AI can quickly interpret information, it also may help accelerate the way health systems and providers interact with patients, especially from a first response perspective. Generative AI can help with streamlining care and processes — from deciphering minor health issues where emergency rooms might not be necessary to automatically scheduling an appointment or emergency room visit if a fever doesn’t drop within a 48-hour period.
Generative AI is also increasing the sophistication of chatbots by providing patients 24/7 access to a medical knowledge base. These chatbots can be trained to respond to specific prompts or questions from patients offering human-like conversations. Providing this level of human-like interaction and knowledge may make them appear superhuman, which could be perceived as “dangerous” to some. However, there is work underway around AI security and hallucinations to provide an experience to which people can relate and that they can trust. One possible mitigation for this is to use it for areas where a solution or output can be easily verified or fact checked afterwards.
Use case No. 6: Continuing medical education
Besides treatment and care, medical personnel can leverage generative AI when summarizing new research papers. Through the analysis of copy, text and images, it can detect relevant information for a specific medical domain, disease or procedure. This makes continuous learning easier for doctors without having to process the full content and instead focus on what is relevant for their domain or interest.
Use case No. 7: Alleviating administrative burnout and reducing costs
There’s so much burnout at every level of our healthcare infrastructure, with administrative and operational tasks being two of the biggest culprits for that inefficiency. Generative AI can support with streamlining appointment schedules, note taking during appointments, and managing billing and collections. Reducing administrative efforts will only make organizations more productive and drive overall operational efficiencies.
Generative AI is only growing in popularity, especially in healthcare, and will continue to change how we work in our day-to-day jobs. Although there may be some hesitancy around it, it’s evident that it will only continue to evolve – so staying informed on how to safely capitalize on it will help your organization grow and thrive. Like any other powerful tool, safety and security needs to be prioritized but should work together as generative AI research advances at a rapid pace.
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