How are Medical Technologies & AI Used in Preventative Healthcare?
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“Imagine a device that adjusts insulin levels throughout the day based on real-time data, providing tailored treatment for diabetes patients. This kind of technology not only personalizes care but also adjusts to a patient’s daily activities, offering a more responsive and effective treatment paradigm.”
Michael Rees, Head of Clinical Delivery at BIOS Health
In the rapidly evolving healthcare landscape, adopting advanced technologies plays a pivotal role in shifting the focus from treatment to prevention. Among these technologies, artificial intelligence (AI) stands out as a transformative force. AI algorithms are increasingly harnessed to predict, prevent, and manage potential health issues, offering a proactive approach to medicine that promises to enhance patient outcomes and optimize healthcare resources.
Michael Rees, head of clinical delivery at BIOS Health, spoke with us about the intricacies of AI technologies in prevention, the challenges of developing these tools, recent advancements, and the ethical considerations necessary for their integration to provide a comprehensive view of how AI is reshaping the approach to health and wellness.
Meet the Expert: Michael Rees
Michael Rees, Head of Clinical Delivery, BIOS Health
Michael Rees currently serves as the head of clinical delivery at BIOS Health.
With an engineering background specializing in designing and scaling medical devices, Rees is pioneering AI-powered neural interfaces for optimized therapeutic dosing. He holds an undergraduate degree in biomedical engineering from Duke University and MBA from Cambridge Judge Business School.
The Role of Medical Technology in Preventative Healthcare
Medical technology is transforming how we approach preventative healthcare through groundbreaking work in fields like neural engineering. This cutting-edge field focuses on decoding the signals of the nervous system, including the brain, spinal cord, and peripheral nerves, to preemptively tackle chronic diseases.
“Our work at BIOS Health is dedicated to neural engineering, where we work to decode the signals in the nervous system to understand and treat chronic diseases,” explained Rees. “The nervous system provides real-time information about the state of virtually all of your organs, and neural dysfunction is implicated in a host of chronic diseases—from brain-related conditions like depression and Parkinson’s to organ-specific issues like heart failure and diabetes.”
By harnessing the power of AI and machine learning, Rees is developing tools that monitor and actively modify neural signals to manage, halt, or even reverse disease progression.
The role of AI in this process is multifaceted, involving complex algorithms that analyze neural data to predict and intervene in potential health crises before they manifest as full-blown medical conditions. This proactive approach is made possible through sophisticated hardware, software, and machine learning tools that interface directly with the nervous system.
“Our tools record the signals passing through the nervous system and then stimulate the nerves to alter neural firing rates, which can change the function of different organs and ultimately treat diseases,” Rees explained.
This method targets the immediate symptoms and addresses the underlying causes of diseases, offering a more sustainable and effective healthcare solution. Integrating these AI-driven technologies into clinical settings is meticulously managed to ensure safety and efficacy.
“As head of clinical delivery, my role involves the clinical translation of our tools, working on documentation, technical files, testing, and validation,” he added. “This process includes close collaboration with clinicians to tailor the technologies to real-world needs and conducting rigorous trials to validate their effectiveness.”
Currently, BIOS Health is initiating a clinical trial in the Netherlands—the first to apply their technology in a real-world setting—and is engaging with clinicians in the United States to explore further applications. This careful, methodical approach ensures that once these tools reach the market, they are innovative, safe, and well-suited to improving patient outcomes through preventative measures.
Challenges in Developing AI for Preventative Healthcare
Developing AI technologies for healthcare, particularly in the specialized field of neural engineering, poses a formidable array of challenges.
“Neural engineering involves incredibly complex technical challenges, especially when dealing with the brain,” explained Rees. “Unlike a computer, you cannot simply dismantle the brain and test out its components without risking harm to the individual. Our work involves careful recording and interaction with neural signals, which are inherently messy and difficult to interpret.”
Rees elaborated on the intricate nature of these interactions, highlighting the sensitivity required when dealing with neural pathways. “The brain’s complexity is unmatched, and each intervention must be precisely calibrated to avoid adverse effects. This requires not only a deep understanding of neurology but also advanced engineering to create devices that can interact safely and effectively with neural tissues.”
This dual requirement for medical and technical expertise poses a significant barrier to entry, demanding a high level of interdisciplinary collaboration and innovation.
Furthermore, the variability in human neural architecture adds another layer of complexity. “No two brains are exactly alike, which means that solutions need to be highly adaptable and customizable,” Rees noted.
The challenge here is developing AI systems that can learn and adapt to individual differences in real time, ensuring that interventions are personalized and effective. This need for bespoke solutions complicates the design, testing, and implementation processes, making scalability and universal application challenging tasks.
“We deal with issues from how we record signals and what materials we use to interface with the nervous system to the application of algorithms and filtering methods to make sense of those signals,” he added.
This technical complexity is compounded by the need to personalize treatments. Targeting and dosing, for example, are particularly challenging as they require precise adjustments to fit individual needs.
“There’s a massive number of settings in devices like neural stimulators, and finding the right ‘dose’ of stimulation for each patient is like searching for a needle in a haystack,” Rees explained. BIOS employs sophisticated algorithms to navigate this vast search space efficiently, tailoring therapies to individual patients—a process that’s technically demanding and critical for effective treatment.
Then, on the business side, there are some potential hurdles to bringing this kind of innovative research to market.
“Developing new medical devices, especially invasive ones, is costly and time-consuming, often taking years and significant financial investment to navigate the regulatory pathways,” Rees acknowledged. Moreover, the focus on hardware is shifting towards software solutions that can provide decision support tools for clinicians, emphasizing the need for regulations that keep pace with technological innovations.
“Building a business case for technologies like brain-machine interfaces is difficult due to the need for extensive clinical data, which is expensive to acquire,” Rees pointed out. The costs of pivotal clinical trials can range from $15 to $50 million, he explained, and the market for certain applications, like treatments for amputations or ALS, is relatively small, complicating the financial viability of these innovations.
Ethical Considerations and Safe Integration of Predictive Technology in Healthcare
As predictive technologies become increasingly integrated into healthcare systems, ethical considerations and establishing trust with clinicians emerge as significant challenges.
Rees articulated the nuanced tension between different types of AI—”explainable” AI, which offers transparency about its decision-making processes, versus “unexplainable” AI, which operates as a black box. This distinction is crucial as it directly impacts clinicians’ willingness to adopt these technologies.
“Clinicians need to understand how decisions are made by AI to feel confident in its recommendations and to ensure that these recommendations are appropriate for their patients,” Rees explained.
The goal of developing AI tools for healthcare is to create decision-support tools that enhance clinicians’ capabilities without replacing their judgment. “Rather than replacing the clinician in the decision-making process, our AI tools are designed to augment their skill set, providing them with additional insights to make the best possible decisions,” Rees said. This approach ensures that AI tools support healthcare professionals by effectively enhancing their ability to assess and respond to patient needs.
He highlighted the hypotensive probability indicator developed by Edwards Lifesciences as a pertinent example. This machine-learning algorithm monitors patients post-surgery and predicts the likelihood of a hypotensive event within the next 20 minutes. “The system doesn’t take actions on its own but alerts the clinician, who then makes the final decision on the course of treatment,” Rees pointed out. This model exemplifies the “clinician in the loop” approach, where AI provides critical information but leaves the final decision-making to medical professionals.
Moreover, another critical aspect of integrating AI into healthcare is addressing the biases inherent in machine learning models. These models are only as good as the data they are trained on, which can sometimes reflect biases related to age, gender, or race. Although demographic issues initially existed during development, the engineering team worked to rectify the disparity and seems to have solved these problems. Now, the device itself is FDA approved, and has been demonstrated in peer reviewed randomized study to have accuracy on par with the gold standard cardiac measurement methods.
The potential for AI in healthcare is vast in terms of individual patient care and managing broader health outcomes. “Machine learning provides an opportunity to aggregate, synthesize, and deduce insights from large-scale health data in ways that were not previously possible,” said Rees. This capability allows for more effective risk management and preventative measures, ultimately improving long-term health outcomes.
AI can significantly enhance healthcare delivery by navigating these ethical challenges and focusing on safe integration. However, ensuring these technologies are used responsibly and transparently will be crucial in maintaining trust and efficacy in clinical settings.
Future Directions in Predictive Healthcare Technologies
The future of predictive healthcare technologies is poised for substantial growth and innovation, particularly in neural engineering and bioelectronics. In recent years, there have been remarkable advancements in treating conditions that were once considered intractable.
Looking forward, Rees explained, the focus is increasingly on developing closed-loop systems that can adapt treatments in real time based on continuous patient monitoring.
“Imagine a device that adjusts insulin levels throughout the day based on real-time data, providing tailored treatment for diabetes patients,” suggested Rees. This kind of technology not only personalizes care but also adjusts to a patient’s daily activities, offering a more responsive and effective treatment paradigm.
One particularly promising area of research is targeting the vagus nerve, which impacts a wide range of bodily functions. “A single device that can modulate the vagus nerve might treat multiple conditions, from cardiac symptoms to depression and autoimmune diseases,” Rees said. This approach could revolutionize treatment strategies by providing a multi-functional platform for therapeutic intervention.
Companies like Motif Neurotech are at the forefront of developing minimally invasive devices that stimulate the brain to treat forms of depression resistant to traditional pharmaceuticals. These advancements highlight a shift toward more targeted and less invasive treatment options, potentially transforming patient outcomes dramatically. Concurrently, the field of brain-machine interfaces is experiencing significant growth. For instance, Blackrock Neurotech has created devices enabling individuals with ALS to control computers and communicate directly through their thoughts, achieving typing speeds comparable to conventional methods. “The algorithms behind these technologies are incredibly sophisticated, opening up entirely new forms of communication for those who need it most,” noted Rees.
On the administrative side, AI is making strides in reducing the healthcare system’s bloated costs. “AI can automate routine tasks, like generating meeting summaries and managing health records, which can drastically cut down on administrative expenses,” Rees pointed out. In the American healthcare system, where administrative costs constitute a significant portion of overall expenditures, AI offers a promising solution to reduce these costs effectively.
Ultimately, the potential of AI to transform healthcare is immense, not only in clinical applications but also in improving operational efficiencies and reducing costs. As this field evolves, the emphasis will likely continue to shift towards integrating AI in ways that enhance the capabilities of healthcare professionals, thereby enriching patient care and improving health outcomes on a broad scale.
Embracing these technologies requires continued collaboration between developers, clinicians, and regulatory bodies to ensure that these new tools are effective but also safe and accessible to all who need them. Integrating predictive technology into healthcare is just beginning, but the horizon is bright with possibilities for transforming how we understand, manage, and treat various health conditions.
