New Wave of Innovation: AI/ML in Medtech
AI/ML Enabled Medical Technology
The FDA recently offered the following insight on AI enabled medical technology: “Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated…More data is better, but only if it is the right data and we fully understand it.”
The data collection and its associated learning into meaningful human action is complex and largely uncharted. This emerging technology has enormous potential, however. Among other things, by reporting accurate, dynamic data and actionable insights to healthcare professionals, it can directly help avoid injury and improve patient outcomes.
AI-enabled medical technology - The Opportunity
At the most basic level, artificial intelligence enhances the patient experience and optimizes clinical staff’s valuable time. AI has the potential to:
- Predict and diagnose disease more quickly than clinical observation alone,
- Recommend the right treatment at the right time, and
- Through deep learning, provide previously undiscovered insights about medical issues
We know from studies (for example where AI systems outperformed dermatologists in correctly classifying suspicious skin lesions) there are inherent advantages and possibilities to unlock. AI systems can constantly learn from successive cases and can be exposed to multiple case data within minutes. Assuming adequate privacy and security safeguards, the data scales and can be shared with other medical professionals to rapidly draw on an even larger demographic sample size. The amount of data a robust AI system can digest and make recommendations on within minutes exceeds what a clinician could evaluate across a lifetime.
The ability to scale and securely share data could be transformative in predicting outbreaks by drawing statistically valid sample sizes to test procedures and accelerate validation of treatment efficacy. Topically, check out an example of this from Johns Hopkins with the current global virus outbreak applying GIS and data:
Consider this: *PWC estimated that in 2013, the volume of health-related data had reached over 4 zettabytes – that’s 4 trillion gigabytes - and that some projections put this exponential growth rate at 10 times that by 2020. This is where AI/ML is game-changing, as, “staying current with and being able to access this data is simply beyond the scope of any human individual, no matter how capable or intelligent.”
Reactive to Predictive Care
As we move from a reactive to predictive model of healthcare delivery, AI-enabled medical sensing technology is part of a fundamental change in the culture and organization of health care service: among other things, this technology has the capacity to predict and detect signs of pressure ulcers long before the human eye can process what’s happening. The identification and diagnoses of an impending pressure ulcer is tough for humans to catch during the initial stages, such that, prior to this kind of sensing, they generally weren’t diagnosed until it was too late.
Machine learning can assimilate vast amounts of data in minutes, thereby acting as a prompt for doctors and clinicians to make individualized, patient-specific decisions regarding detection, prediction and prevention. Treatment plans become optimized to minimize recovery time, and the best possible outcomes are achieved from both care and financial perspectives as a result.
AI’s ability to transcend human intelligence by accelerating data retention, pattern matching, retrieval and processing into smart, actionable, proactively preventive care is the future of medicine.
AI-Enabled Medical Sensors
At XSENSOR, our goal is to provide clinicians with dynamic, real time and accurate data that directly improves patient safety. Our surgical table and patient bed technology use Intelligent Dynamic Sensing to continuously monitor skin while AI-powered algorithms offer actionable data that directly informs advanced prevention strategies to identify pressure points before the skin’s integrity is breached.
In practice, in the operating room where pro-active positioning and monitoring is key to pressure injury prevention, our intelligent sensors can already generate and record real-time images of body surface pressures that surgical staff can use as part of their perioperative patient safety plan. Once the patient is transferred to a hospital bed outfitted with the Patient Turn System for recovery, hospital staff can refer to a monitor display of the real-time pressure mapping of the patient’s body against the bed as a means to mitigate the risk of pressure injuries afterward.
What AI Won’t Do
This isn’t a future of runaway robots or AI taking over; Hal from 2001 A Space Odyssey isn’t in control of health care delivery. The reality is that the greatest challenges in healthcare will always be focused on the human element: it is, after all, a domain focused on how people care for and heal other people.
Almost everything created is built for the benefit of humans, either directly or indirectly, and AI enabled medical technology is no exception. It’s meant to assist, not replace clinicians in achieving superior remedies that are specific to individual patient needs and outcomes. Human oversight is critically important as AI evolves to proactively conserve valuable human resources. Menial tasks should (in time and with empirical support) be allocated to machines.
Technology will never entirely replicate a doctor’s judgment and ability to provide care and treatment. The machine learning output must still be analyzed by someone with domain knowledge, otherwise, trivial data may be interpreted as essential and essential data as trivial. These relationships then have to be translated to clinical actions.
AI-enabled medical technology – The Future (the now?)
With ever more data collected and assimilated, artificial intelligence and the associated machine learning will continue to improve at a rapid rate, allowing for the best clinical outcomes and the best patient experiences possible.
Soon, it will be common to find ML-based applications embedded with real-time patient data available from different healthcare systems in multiple countries, increasing the number and competency of treatment options, some of which may have been unavailable before.
With daily advancements in the Internet of Things, the healthcare industry is still discovering new ways to use the volumes of data being collected to apprise tough-to-diagnose cases and improve diagnosis and medication. AI and ML enabled medical products and platforms are leading towards a previously unimagined kind of precision medicine. Ultimately, the vision is that these technologies one day predict - and therefore prevent disease – before it even occurs.
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