By Bryce Beamer
Among the current digital age's most optimistic developments is the application of artificial intelligence (AI) and the Internet of Things (IoT) to improve healthcare outcomes. Improvements in hardware technology, including low-power sensing, edge computation, and wireless communication systems, enable these advancements. Connected health products are at the forefront of this changing face of healthcare.
Intricate mechanisms built into hardware make better patient outcomes possible, but there are also potential pitfalls that stakeholders in this rapidly expanding industry need to avoid.
Uses of AI in healthcare – medical imaging, diagnosis & prevention
AI offers healthcare solutions that were unthinkable even a few years ago. One avenue of advancement is through machine learning algorithms that model and understand complex biological data. Within the field of medical imaging (which includes MR, CT, and ultrasound), AI offers opportunities to improve imaging accuracy, streamline acquisition, and even assist in highlighting abnormalities.
Beyond imaging, AI can aid in predicting diseases, diagnosing conditions, personalizing treatment, and discovering novel drug chemistry. Many experts see opportunities to increase healthcare facility operational efficiency, improve customer experience, and enhance patients' and providers' physical and mental health.
Perhaps the biggest opportunity for the use of AI in healthcare is through improved preventative care and early intervention. To enable this shift toward preventive care, we must monitor patients more regularly without overloading healthcare facilities and infrastructure. One way to do this is by transferring these capabilities out of the hospital and into the home.
The Internet of Things: medical wearables rapidly expand diagnostic capabilities
At-home IoT healthcare devices, also known as medical wearables, are the key to a more sustainable healthcare future. Low-power mobile health monitoring leverages existing electronic hardware designed for mobile and wearable devices. Creating a network of complementary IoT medical devices embedded with low-power sensing and data processing will aid in capturing vast arrays of biometric data that could help drive preventative care. Existing sensor systems measure ECG , blood glucose , blood pressure , optical heart rate, body motion, oxygen saturation, and respiration rate — all useful metrics in building a comprehensive picture of health.
Wi-Fi and Bluetooth facilitate data transmission from these devices to healthcare providers, ensuring the availability of real-time patient data for analysis and decision-making. This increased efficiency enables early intervention and supports remote patient monitoring for treatment compliance and rehabilitation, reducing hospital visits and improving healthcare accessibility.
AI and IoT are complementary medical technology
Together, AI and IoT enable accurate biometric data collection in a patient's normal environment while allowing for deployment to large populations. Distributed computing and data filtering help to reduce central computing demand and manual interventions.
Further implementation of AI and IoT in healthcare will help doctors better understand the complex interaction between our biological systems and larger data sets collected in a patient's natural environment. Many technology companies recognize that these large data sets will be the key to future health innovation.
Barriers to AI and IoT adoption in healthcare
While AI and IoT promise a transformative impact on healthcare , several potential pitfalls, including healthcare data security, could limit their potential.
Data security and IoT medical devices
Healthcare data privacy and security remain paramount concerns. The increasing interconnectedness of devices and healthcare data on the cloud makes systems vulnerable to cyber threats or data exposure. Stakeholders need to ensure robust cybersecurity measures are in place to protect patient data while defining pathways for data collection and sharing that allow for innovation.
Data reliability in at-home measurement
The successful application of these technologies requires high-quality, reliable data. Inaccurate or incomplete data can lead to incorrect predictions or diagnoses, potentially harming patient outcomes. We must look for ways to verify quality data collection from at-home medical wearables and incorporate reasonable redundancy and fail-safes to ensure patient health.
Given that sensing technology has advanced based on existing medical standards, bringing this technology into the home precludes the direct supervision of medical professionals. To solve this problem, we must use intuitive design and active feedback loops to provide clear and fail-safe guidance to the patient.
New technology expenses
Implementing AI and IoT in healthcare requires significant investment in infrastructure and training. Many healthcare institutions may not have the financial capacity to adopt these technologies or the technical expertise to use them effectively. Healthcare systems must adapt and invest in adopting these technologies at scale. Making IoT both financially accessible and scalable is essential to ensure its adoption and success.
A more connected, healthier future
The potential of AI and IoT in healthcare is immense, providing opportunities for improved patient monitoring, diagnosis, and treatment. As we navigate this new world, it's crucial to recognize and mitigate potential pitfalls, aiming to maximize patient outcomes and ensure a healthier future.
Learn more about how Arrow can help guide you to the smart, secure future of healthcare.
