The medical field may arguably be the most stressful career path, and during the last few years the world has seen increasing amounts of workers leaving the industry. A Washington Post/Kaiser Family Foundation survey recently showed that during the pandemic, 1,327 front-line healthcare workers revealed that medical burnout had reached epidemic proportions and the answer may be Artificial Intelligence.
Prior to the pandemic, investors noticed that adoption for medical imaging AI was slow due to doctors’ own hesitation to learn technology that may impede heavy workflow. At the peak of COVID-19, adoption of the technology skyrocketed as industry professionals accepted any help that was offered, and funding for startups rose exponentially, going from $348 million to $1 billion between 2020 and 2021. Medical imaging AI used to aid in diagnostics reached an all-time high demand when doctors’ offices and clinics became need-based. With so many people in need of medical care, it’s easy for serious illnesses to go undetected, and this AI technology could help pre-screen patients and scan images to help find problems typically undetectable to the human eye.
CEO and founder of medical imaging AI startup Eyenuk Kaushal Solanki stated, “Humans are missing a lot of diseases because there is an inherent mindset where they’re thinking, ‘Can I treat this patient tomorrow?’ And that’s not the preferred threshold.”
Despite the spike in interest during the pandemic, now that things have settled investors are seeing a decline in adoption as doctors become hesitant once more, which may delay the future of medical imaging AI.
“It’s actually not a problem of the technology not being sophisticated enough,” said biotech investor at Wing VC. Sara Choi, “It’s an adoption problem, and really proving out the use cases to convince providers that there’s business value as well as clinical value to these solutions.”
According to Crunchbase News, the American College of Radiology found that most AI platforms aren’t independently validated, and healthcare providers need the models to “work transparently and be explainable,” according to assistant professor of pharmaceutical and health economics at the University of Southern California William Padula. “The fear here is that while the programmer knows what they’ve done to create the model, it’s unclear how exactly it’s looking at the patients.”
Whether or not AI takes off in the medical field, doctors have expressed hesitance and laid out what needs to be addressed in order for medical imaging AI to be adopted by healthcare providers. Though it seems delayed for now, AI could possibly become more medically prevalent in the future.