Artificial intelligence (AI) is currently the hottest trend in technology, with new use cases emerging by the month. This is especially true of generative AI, a class of algorithms designed to create novel responses based on data input. The powerful reach of AI is visible in the way the technology has spread to industries with demanding regulatory and functional requirements, healthcare included.
The beginning of healthcare AI usage involved simple data management systems. In the years since, complex predictive models have risen to the fore. A rapid trajectory has taken healthcare AI from straightforward algorithms that manage electronic health records (EHRs) to advanced neural networks capable of assisting in diagnoses. The ongoing rise of generative AI fits this high-growth pattern.
To better understand the use of generative AI in healthcare, it's important to define the technology. Plainly stated, generative AI algorithms learn from data resources and use that input to create content or data that resembles the raw material. The output of a generative AI model is new and previously unseen, rather than being taken directly from the source data.
Generative AI models can interpret and produce data across formats, including text, graphics, video and audio. Large language models (LLMs) are a prominent subset of generative AI algorithms that process text.
The most advanced generative algorithms can recognize patterns, make predictions and suggest novel solutions to problems. Early versions of these AI tools have proven popular across industries, in both internal roles, such as summarizing meetings, and external jobs, including customer service. Healthcare companies can put the algorithms to ambitious, boundary-pushing use.
Potential use cases for generative AI in healthcare encompass all areas of organizations, from frontline patient care to research and administration. Since these AI tools can produce a wide variety of responses based on the data they're fed, it's easy to see how they can cross departmental boundaries.
Some roles receiving early attention include:
A huge number of data points affect a person's specific patient profile. While analyzing all this information to produce a customized treatment plan may have been difficult or impossible for earlier digital systems, AI algorithms are up to the task, allowing providers to move away from "one-size-fits-all" treatment.
Information affecting ideal healthcare options includes:
Research has pointed to AI as a tool for identifying early risk factors of chronic diseases, including diabetes, cancer and cardiovascular diseases. Advanced algorithms can take patient data from multiple sources, prominently including direct input generated by wearable smart devices.
With a picture of individual health and risk factors based on analysis, healthcare providers can develop personalized treatment plans that address a person's specific needs and can slow the progression of diseases.
The field of mental health care is facing some of the same issues as more generalized care, which means similar AI solutions may be applicable. Thus far, this has meant early detection tools based on patient data, leading to the creation of personalized treatment plans.
Due to generative learning's ability to provide novel, natural-language output, applications powered by AI are also being used directly for therapeutic interactions. Having access to digital tools that can provide cognitive behavioral therapy and other types of mental health care increases the availability of these services. That rising access can also help to destigmatize the use of mental health treatment.
Diagnostics is one of the areas with the most to gain from generative AI model use. Doctors equipped with these algorithms can increase the speed and accuracy of early disease detection. AI tools can use thousands of medical images as their training data set, allowing them to identify patterns undetectable by humans. AI has already begun to assist with early detection in radiology.
Several branches of AI technology contribute to enhanced diagnostics performance. Predictive modeling, machine learning (ML), deep learning and federated learning are all among the technologies used by care providers. In the years ahead, the integration of AI with edge computing devices promises further transformation.
The drug development process is known for long timelines and high costs, making it a good candidate for digital transformation. AI has already begun to have an impact in this field. An advanced AI algorithm can make predictions about the way chemicals will interact with biological compounds.
Guided by detailed projections and predictive models, researchers can shorten their timelines to identify new drug candidates and bring them to trial. Making the early parts of the overall development process faster and more efficient can eventually lead to impactful treatments hitting the market sooner.
Generative AI systems fed continuous streams of data are potentially transformative patient monitoring tools. A subtle change in patient vital signs that may go unnoticed by staff members can trigger an automated alert and intervention to provide preventative medicine. In critical care, these systems can prove especially impactful.
One specific branch of AI can help with this side of healthcare: federated learning. This is an approach designed to allow generative models to learn collaboratively, considering input from wide networks of distributed edge devices and enabling fast, locally processed decisions. The key factor behind federated learning is that it provides this level of AI model training with less implications for data privacy.
Training staff in managing difficult, high-stakes scenarios has always been an important need in the healthcare industry. By providing realistic but risk-free simulations, AI tools can assist with learning. These algorithms can also help healthcare organizations and schools of medicine keep up with the demanding nature of training today.
A few factors are affecting medical learning:
The sheer volume of medical information today strains the human capacity for learning beyond the limit. The doubling time of medical knowledge, which was 50 years in 1950, was projected to reach 73 days by 2020. To study all new primary care literature, a student would have to study over 29 hours every 24-hour weekday. AI can help cut through the large amount of resources to find the most useful information.
Generative AI has the potential to significantly increase efficiency among care providers. Scheduling, billing and record-keeping can all benefit from AI assistance, streamlining workflows and freeing up employees to provide more patient care. The result is lower wait times, better service quality and happier patients.
Products in this space include MedLM. This set of models from Google, built on the Med-PaLM 2 large language model (LLM), can assist with tasks in research and development, medical note management and customer experience. At HCA Healthcare, a MedLM pilot is designed to generate hands-free medical notes based on physician and patient conversations.
Introducing new technology into a sensitive area like healthcare, with a direct influence on patients' well-being, is fraught with challenges. Achieving generative AI's great potential will involve overcoming pressing issues, including:
The next few years of development will involve grappling with these challenges to make sure the AI revolution is a positive force in healthcare IT.
Since AI technology's capabilities are expanding so rapidly, there is potential for further groundbreaking change in the near future. More AI algorithm refinement can lead to sophisticated new diagnostics, improved treatment personalization and more in-depth patient care management.
It's important to note that research has shown AI is not a replacement for clinicians but rather a tool to improve human effectiveness. Now, healthcare IT professionals must clear the path for AI adoption within their workplaces.
The research indicates that to achieve that goal, these essintial steps have to be completed:
If organizations can deliver on these objectives, they can push the use of generative AI forward in healthcare, seizing on the technology's momentum.
Both the present and the future of healthcare AI use are promising, and when care providers are able to make these systems work for them, exciting outcomes can result. Engaging with an expert partner like Transcenda is one way to help your business make progress with these powerful new applications and technologies.
You can see what Transcenda has accomplished in the field in our engagement with Medidata. The data science organization is able to generate powerful insights in the pharmaceutical, biotech, medical devices and diagnostics fields. You, too, can start putting data to use with the next generation of AI.
Contact Transcenda to learn more about how we can help your organization in its development efforts.
