Seeing the Forest Through the Trees: Computer Vision in Healthcare Diagnostics
In today’s world, the line between science fiction and reality blurs rapidly. So much so that I’m at the point where I second guess almost…
In today’s world, the line between science fiction and reality blurs rapidly. So much so that I’m at the point where I second guess almost everything that I see and most of what I hear.
That said, even me, with my healthy skepticism, can’t help but feel like we are actually at the nexus of numerous technological revolutions — from self-driving cars to intelligent personal assistants. But, nowhere more exciting than in healthcare and, specifically, computer vision, which is rapidly shaping the future of healthcare diagnostics.
Understanding Computer Vision
In layman’s terms, computer vision is a field that trains machines to interpret and infer from visual data, similar to how humans use their eyesight and brain in tandem. For medical diagnostics, it translates to machines examining medical imagery and pinpointing anomalies or patterns that might elude even the most trained human eyes.
Why is this so revolutionary?
In the continuum of medical history, the transition from film-based X-rays to digital imaging marked a significant leap. It revolutionized medical diagnostics because digital imaging offers instant, high-quality images that can be easily manipulated, shared and integrated seamlessly into electronic medical systems. As a result, the transition to digital not only enhanced patient care but also streamlines and modernizes medical workflows.
Yet, even despite being an order of magnitude better than their film-based counter parts, digital images remained firmly tethered to human judgement, which, by its very nature, is prone to inconsistency, emotions and fatigue.
Insert computer vision.
A 2021 study illuminated the prowess of computer vision, revealing that Paige’s, the first company to receive FDA approval for clinical AI application in digital pathology, AI-based prostate cancer detection system has the ability to correctly identify a negative test with 100% accuracy (negative predictive value). Additionally, their product mindbogglingly boasts the ability to reduce cancer detection errors by 70%, a 65.5% reduction in time to diagnosis and a 39.9% reduction in second opinion requests.
Computer vision’s secret sauce? It processes images at resolutions beyond human capabilities and uses algorithms to find patterns and anomalies. Essentially, it’s like giving a doctor a super-powered microscope and an encyclopedic knowledge of diseases.
Oh yeah, and that doctor never gets sick, tired, mad or is in a rush to get to their kid’s soccer game.
That is the game changer.
Today’s medical diagnostics generate a staggering amount of data. A single patient’s full-body MRI can produce over a terabyte of data. Multiply this by the millions of scans conducted globally, and you get a data set too vast for any human or team to comprehend and synthesize in any real, meaningful way.
This is where computer vision thrives. It can parse through unlimited amounts of data, searching for patterns. It doesn’t tire, doesn’t get swayed by emotions, and doesn’t need breaks. Day in and day out, it provides consistent, accurate analyses.
This, in turn, can lead to early detection, accurate diagnosis, and personalized treatment plans… giving us that pathway to increasing life expectancy.
The Party’s Already Started
This revolution is already underway having already reshaped traditional workflows, promising a new dawn for patient outcomes and care efficiency. Below are a few sectors I’ve come across that are being impacted:
Radiology: Once confined to static images and human interpretation, radiology now employs AI to analyze and interpret complex medical imagery. Traditional workflows have been upended with the ability to rapidly pinpoint anomalies that could be overlooked by the human eye. Companies like Aidoc and Nanox stand out, using AI to assist radiologists by rapidly analyzing images and highlighting potential issues. Take, for instance, the case of Aidoc, which, in one month alone, flagged 77 patients with acute intracranial hemorrhages, showcasing the unparalleled potential of AI in radiology.
Pathology: The intricate science of studying diseases, pathology has been transformed by AI, making disease detection more precise and efficient. Companies like Paige, PathAI and PathologyWatch have been at the forefront, leveraging deep learning to ensure that disease diagnoses are faster, more accurate, and consistent. In a 2019 study, an AI-driven system could identify pre-invasive breast cancer biopsies with 89% accuracy, compared to 70% by human pathologists, paving the way for early and effective interventions. I can only imagine the predictive models have gotten better since then.
Dental: Traditional dental examinations relying on subjective evaluations are now enhanced by AI-powered dental imaging tools. Companies like Pearl, with their ‘Second Opinion’ software, provide automated detection of dental conditions, from cavities to gum diseases. Notably, Pearl’s software identifies early-stage cavities that were missed during a routine checkup. This early detection allowed for minimal intervention, saving the patient from potential extensive treatments in the future.
Preventive Health: Shifting the focus from disease treatment to prevention, AI is carving a niche in preventive health. Traditional health screenings, often cumbersome and reactive, are becoming more proactive and personalized, thanks to AI’s predictive capabilities. Pioneering this shift are companies like Prenuvo and Ezra, offering AI-driven, comprehensive body scans. Consider the case of an Prenuvo user, Maria Menounos, who opted for a full-body MRI scan. Prenuvo’s scan highlighted a 3.9mm mas on her pancreas, in which a subsequent biopsy confirmed it was stage 2 pancreatic neuroendocrine tumor — a rare form of pancreatic cancer. Instances of early intervention like Maria’s underscore the profound impact of AI in moving from reactive treatments to proactive health preservation.
AI’s integration into healthcare diagnostics is not just transformative; it’s revolutionary. As algorithms become more refined and data sets more extensive, AI’s potential in enhancing patient outcomes and revolutionizing care pathways is limitless. Radiology, pathology, dental, and preventive health stand as testimony to the early successes and vast potential that AI promises. As we stand on the precipice of this AI-driven medical revolution, one thing is clear: healthcare will never be the same again.
Imagine a world where access to top-notch healthcare isn't a privilege but a ubiquitous right. The dream? Leveraging the power of AI to detect, diagnose, and even dictate treatments, ensuring that lives are not just saved, but, potentially, saved early.
Let's not be under any illusions, though. The path to this healthcare utopia is paved with challenges, sweat, and, yes, a few tears. But here's the silver lining: the heavy lifting is well underway. The digital scaffolding is being erected as we speak, and these companies are ascending, fueled by ambition and a vision that promises a brighter, healthier future.
So, here's where the rubber meets the road. If you're reading this, you're either at the forefront of this AI-led healthcare renaissance or you know someone who is — and I’d love an introduction to help be a part of the sea change.