How Generative AI is Transforming Healthcare Access and Delivery

The growth of generative AI, in both usage and valuation, has generated a tremendous amount of hype since the day ChatGPT was released to the public. Yet lost amongst the platitudes of generative AI’s ability to transform industries, are essential conversations around how to apply this form of AI within organizations. How will generative AI integrate with existing structures? What new models for value creation will be introduced? Who will be driving this change?

In healthcare, generative AI is already beginning to demonstrate how it can reimagine the industry in new and exciting ways. Beyond easing administrative burdens and scaling tailored patient communication, generative AI is accelerating drug discovery and clinical trials. 

As a national leader in health-focused vision care, VSP Vision is monitoring the pulse of healthcare transformation. Generative AI is at the forefront; its impact reverberating across healthcare systems, providers, payers, and more. In fact, there are already several instances of the technology’s presence in eye care, from scaling readings of retinal scans to personalizing eye health content.

In the VSP Global Innovation Center’s (GIC) latest Futurist Report, The Future of Generative AI in Healthcare, we spotlight the currents and startups behind the technology’s possibilities and potential.

Throughout the report, the GIC explores five rising trends that will shape the future of generative AI in healthcare:

• System cohesion makes shared care possible. According to a study in the Journal of the American Medical Association, failures in care coordination” cost between $27.2 and $78.2 billion per year. Data fragmentation across the healthcare continuum is driving a need for tools and solutions that can ease and accelerate the unification of various processes and practices. 

New AI solutions will improve efficiency across the many moving parts of the healthcare system, while other startups and technologies will focus on specific components, such as accelerating clinical trials or translating unstructured data at scale. This will have a ripple effect on the healthcare industry, making it more cohesive and less expensive to run.

Explained Jay Anderson, Head of Emerging Technology with VSP Global Innovation Center, “While generative AI can certainly translate and summarize healthcare data at scale, it can also make data immensely more shareable, helping driving cohesion throughout the healthcare system.”

• Empowered patients prompt better outcomes. A study from Maestro Health asserts that nearly 40 percent of Americans feel unsupported in understanding their healthcare, while 70% state that the system is hard to navigate

As patients struggle to navigate and find support in the healthcare system, AI innovations are enhancing the patient experience. Supported by new tools, such as next-gen chatbots and symptom checkers, a future is possible where patients will move from being simply recipients of treatment to active participants in their care journey, leading to improved outcomes.

• In-practice solutions alleviate provider burnout. According to a Bain and Company survey, one in four clinicians are thinking about leaving the healthcare profession, with 89% naming burnout as the main reason

Paperwork and repetitive administrative tasks contribute to healthcare worker burnout. By using gen AI-powered co-pilots and data retrieval to augment operation flows within their practices, physicians will be able to streamline processes to create more time to treat patients.

Added Kent Iglehart, General Manager at Dr. Tavel, “Generative AI has the promise to revolutionize Optometry, freeing our doctors to focus more on patient care by providing insightful, data-driven diagnostics in a fraction of the time.”

• Embedded generative AI transforms medical devices. Researchers and startups are embedding AI and generative AI into medical devices to enable new modalities of treatments. Within medical imaging tools, generative AI presents opportunities to automate and improve the accuracy of medical image analysis, helping power precise diagnosis, treatment planning, and disease monitoring.

This enhancement within imaging has also extended to eye care. Earlier this year, researchers developed an AI model similar to ChatGPT that can detect eye disease and risk of Parkinson’s from retinal images captured by a fundus camera.

• “Big, bad AI” supports good behavior. Because AI tools have been known to perpetuate bias, discriminate against certain demographics, and pose privacy risks, there is skepticism around its potential to do good. 

However, startups are focusing on building and following ethical AI policies, such as algorithmic empathy, synthetic patient data, and greater compliance, as the technology becomes more widespread. According to the Brookings Institute, roughly 75% of AI companies with over 50 employees have an ethical AI policy.

These trends illustrate how the impact of generative AI could reverberate across the healthcare industry by taking on the burden of time-consuming administration, assisting in disease detection, and improving the quality of care. The full report can be downloaded here.