Assuming you haven’t been living under a rock this past year, you’ve heard all the buzz from ChatGPT and large language models (LLMs), a generative AI solution in its infancy that promises to drive innovation in the healthcare industry by leveraging torrents of data collected from patients, payers, machines, and facilities. This data holds the keys to helping healthcare providers make better decisions, promote consumer engagement, and provide insights that can improve patient outcomes.
GPT-4 is the next iteration of OpenAI’s GPT series, and it has even more advanced natural language processing capabilities than its predecessors. This is a game changer, and at least Microsoft knows it. It has been reported that Microsft invested $10 billion in the ChatGPT for a controlling 75% of the profits until the investment is recouped.
With its ability to process and analyze large amounts of natural language data, GPT-4 could improve healthcare systems’ efficiency by automating routine tasks, analyzing patient data, and providing insights to improve patient outcomes. For example, GPT-4 could be used to automate administrative tasks, such as scheduling appointments or managing electronic health records, which can reduce the workload of healthcare providers and improve the patient experience. It could also be used to analyze patient data and provide insights to help healthcare providers make more informed decisions about patient care, leading to better outcomes. Additionally, GPT-4 could improve communication between healthcare providers and patients by analyzing natural language data from patient interactions and providing personalized responses or recommendations.
Overall, while the exact details of Microsoft’s plans for GPT-4 are not yet known, it’s clear that AI has tremendous potential to improve the efficiency of healthcare systems, and GPT-4 could be an essential tool in achieving this goal.
While this early generation of open AI chatbots is fun for the casual user, the data is still broad and shallow. The future impact of this groundbreaking tool depends on the narrower and more profound data and having a specific outcome in mind. The ability to impact healthcare consumers, practitioners, and administrators’ experience and produce measured results in health engagement and outcomes and total cost of care depends on the use cases targeted and the underlying data needed to train the learning.
For example, Multimodal AI uses multiple modalities, such as text, image, audio, and video, combined with artificial intelligence techniques to analyze and extract information from data. Multimodal AI can improve diagnosis, treatment, and patient care in medicine.
One of the most common applications of multimodal AI in medicine is image analysis. Medical images, such as X-rays, MRIs, and CT scans, contain valuable information that can be difficult for humans to interpret accurately. By using machine learning algorithms to analyze these images, healthcare providers can obtain more precise and reliable diagnoses, leading to better patient outcomes.
Another area where multimodal AI can be helpful is in natural language processing (NLP). By analyzing large amounts of medical records, NLP can be used to identify patterns and extract valuable insights that can help improve patient care. Additionally, NLP can improve communication between healthcare providers and patients by analyzing patient interactions and providing personalized responses or recommendations.
Multimodal AI can also be combined with wearable technology to monitor and track patient health. By collecting data from sensors on wearable devices, such as heart rate monitors and fitness trackers, and using AI algorithms to analyze this data, healthcare providers can gain valuable insights into a patient’s health status and identify potential health risks before they become more serious and costly.
LLMs ultimately provide the ability to move seamlessly between medical images, text, voice, and all data sources (sensors, genome, microbiome, and the medical literature) that will afford the many opportunities shown in the diagram below. New federated data architectures, automated infrastructure, and edge computing solutions will be needed to handle the massive amount of data required to solve latency issues and economic balance not resident in our current monolithic technology stack.
However, to handle whatever comes next in the healthcare industry, healthcare providers must adapt their care delivery models and processes with these technologies as digital care team members. Business leaders must transform their business models, adopting a different approach to this complex ecosystem. This approach should be data-driven, collecting data from many places and partners, promoting consumer engagement, and providing insights to improve patient outcomes. Through generative and multimodal AI solutions, automation can play a significant role in this approach, enabling healthcare providers to streamline operations, reduce costs, and improve patient outcomes.
In conclusion, the healthcare industry has many forces driving the need for change, which was only compounded and accelerated by the pandemic. To guide future innovation in the healthcare industry, healthcare entities must adopt a new ecosystem-level data architecture, and a data-driven approach, leveraging automation solutions like multimodal AI to collect data, provide insights, and improve patient outcomes. As one ecosystem, innovation of new business models, new partnerships, new operating and care models, and use-case-driven application of these technologies, holistically, is imperative. By embracing this new, high-touch, high-tech ecosystem, we can deliver more precise insights for patients, clinicians, and care teams across the health system, leading to improved healthcare experiences and outcomes for all. Healthcare is social, open, and good healthcare is a human right. The future of healthcare is up to each of us. With many uncertainties in our world, one thing is for sure, the time to get involved is now.
Looking for Part 1? You can find it HERE