The intersection of healthcare and artificial intelligence (AI) was one of 2023’s most exciting storylines.
Across sector-critical areas including medical imaging and pathology, telemedicine, personalized patient engagement, new drug discovery, remote monitoring, administrative tasks and EHR (electronic health record) data entry, even clinical decisioning and beyond, innovative software tools stepped in to show that improving healthcare and medicine are among the most promising use cases for generative AI.
That’s because healthcare is a giant sector with many segments — meaning there exist different roles AI can play across the ecosystem that each come weighted with different risks and different opportunities.
Those many segments create their own problems. The healthcare industry has historically been riddled with legacy manual processes where doctors, nurses, and administrators alike remain drowning in paperwork while rooting from the sidelines for the pace of healthcare’s ongoing digitization to speed up. But those same inefficiencies represent opportunity areas to a data-hungry innovation like AI.
As PYMNTS Intelligence found, the generative AI healthcare market is projected to reach $22 billion by 2032, offering a number of possibilities for better patient care, diagnosis accuracy and treatment outcomes.
Many of the latest AI innovations, including those designed to help doctors glean insights from healthcare data, and allow users to find accurate clinical information more efficiently, are meant to help put clinician “pajama time” — the time spent on paperwork each night — to bed.
These problems typically cost providers significant amounts of time and resources, and a variety of point-solution were brought to-market this year to address them.
Amazon Web Services (AWS) this year debuted AWS HealthScribe, an AI for healthcare solution that leverages speech recognition and generative AI to generate clinical documentation, saving clinicians time summarizing patient visits and improving care delivery; while its Big Tech peer Google Cloud collaborated with Mayo Clinic to use generative AI in healthcare by providing solutions that to enable clinicians and researchers to find information in a way that is fast, seamless and conversational.
According to the World Economic Forum, hospitals produce 50 petabytes of siloed data per year — equivalent to approximately 10 billion music files — and 97% of this data goes unused, leaving many valuable insights locked away. Such insights that may turn into actionable care delivery in 2024.
Faster, AI-assisted diagnoses have the potential to lead to faster treatment, in turn producing better and more repeatable outcomes. With its ability to analyze vast amounts of medical data, generative AI can assist clinicians in making more informed decisions, identifying patterns that may not be immediately apparent to human practitioners, and even predicting patient outcomes.
But that doesn’t mean doctors themselves are going anywhere. For one, the regulatory uncertainty around AI technology will need to be addressed for the innovation to revolutionize the medical field. And for another, when it comes to the fundamentally critical nature of care delivery, there will always be a need for a human to be in the loop.
“The notion that AI would completely replace a physician or another healthcare provider, to me, is a long way off when I look at the scenarios,” Tom O’Neil, managing director at Berkeley Research Group and former chief compliance officer at Cigna, told PYMNTS in an interview posted in November.
Erik Duhaime, co-founder and CEO of data annotation provider Centaur Labs, told PYMNTS that, “AI for healthcare has never been about replacing doctors, but doctors who use AI might end up replacing those physicians who don’t.”
Despite the potential benefits of AI in healthcare, PYMNTS Intelligence found that 60% of adults remain uncomfortable with the idea of AI-driven healthcare decisions. Surveyed concerns range from biases in AI algorithms to fears that AI may lead to worse outcomes.
“AI has been revolutionizing medicine over the last few decades,” Forward CEO Adrian Aoun told PYMNTS in an interview posted this month. “The problem is that it hasn’t been doing it in the ways that we care about.”
Within areas like drug discovery, remote monitoring, and administrative tasks, many healthcare AI tools are already able to take on the bulk of the work themselves, freeing up humans to focus on other tasks.
Areas like medical imaging and pathology, telemedicine, patient engagement, surgery, and clinical decisioning each represent care delivery avenues where AI is best suited as an aid to human workflows, not an outright replacement.
And getting the right regulatory framework in place will be critical. AI in healthcare represents a promising new frontier, but it also comes with ethical, compliance and privacy considerations and must be approached with care and responsibility.
Already, the U.S. Food and Drug Administration (FDA) has created a new committee dedicated to AI in healthcare, while the White House Office of Science and Technology Policy (OSTP) held a roundtable discussion Oct. 6 in which the administration emphasized the growing priority to “develop and deploy advanced AI tools that benefit the health and wellbeing of all Americans.”
But one of the most exciting applications of AI within healthcare, beyond streamlining physician workflows and bridging historically disparate sector silos, is the capacity it holds for the democratization of care.
“Worldwide, very few people have access to doctors — and the opportunity to have an AI doctor, even if they have just 30%, 50% of an average provider’s knowledge and capability, is still a massive value add,” Beerud Sheth, CEO at conversational AI platform Gupshup, told PYMNTS in an interview posted in November.
As PYMNTS’ Karen Webster wrote at the beginning of the year, AI’s greatest potential is in creating the knowledge base needed to equip the workforce — any worker in any industry — with the tools to deliver a consistent, high-quality level of service, quickly and at scale.
This is particularly true within healthcare, as tailoring AI solutions by industry is increasingly proving to be key to scalability.
Just as doctors are trained in their specialties, so too might the AI healthcare systems of tomorrow.