Artificial intelligence (AI) is rapidly revolutionizing healthcare before our very eyes. From improved diagnostics to faster decision-making and better patient safety, AI allows healthcare providers to gain insights, collaborate more transparently, and, most important, improve patient outcomes. 55% of healthcare providers believe the greatest advantage of AI is improving patient outcomes, and 40% are excited about wearable technology when it comes to continuously monitoring patients.
While there are valid concerns around data storage and privacy, AI in patient care has grown beyond its beginning stages and is poised for wide adoption and exponential growth. 85% of healthcare providers have some sort of AI strategy, and 50% are currently actively using AI. Consumers also acclimate to being more open to AI in their care; for example, 65% of patients said they’d be comfortable using AI in their skin cancer screenings.
How AI Is Revolutionizing Healthcare
The overall AI medical diagnosis market was estimated to be worth $1 billion in 2022 and is projected to hit $5.5 billion (about $17 per person in the US) by 2027, growing at an astonishing almost 40% CAGR in just a few years. With this rapid growth, let’s break down exactly how AI is revolutionizing different elements of healthcare.
Improved Patient Safety
AI is used in various patient safety systems, such as in-hospital care, drug delivery, discharging, and more. AI can make its own decisions and provide invaluable advice and insights to providers and improve error detection. For example, computer vision technology in patients’ rooms could analyze if they fell or were experiencing a stressful event like a heart attack.
Data-Driven Diagnostic Imaging
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Healthcare drives an almost inconceivable amount of data creation, from imaging procedures to patient records to lab results; in fact, healthcare data makes up 30% of all the data in the world. Hospitals perform 33.6 billion imaging procedures annually, yet approximately 97% of data points and insights go unused.
AI’s ability to recognize and process structured and unstructured data has led to nearly 400 Food and Drug Administration approvals of AI algorithms for the radiology field. For example, radiologists use AI to generate 3D models and segment medical imagery. This helps diagnose and treat neurological abnormalities, accurately classify brain tumors, detect breast cancer, and optimize radiation dosages.
More Accurate Physician Decision-Making
AI shows incredible promise in assisting medical providers in making more accurate, informed care decisions. With algorithms, providers can analyze a vast amount of patient data. For instance, machine learning tools can analyze billions of unstructured data points from patients nationwide and draw reasonable conclusions that can then be delivered to care providers. Without AI analyzing this data, human physicians would never be able to aggregate this level to provide better outcomes for their patients.
Early Detection and Continuous Monitoring
Medical wearables assist in the early detection of cancer, heart attacks, sleep disorders, Alzheimer’s, dementia, and more. Not only are these wearables helping with the early detection of diseases by tracking important indicators, but they’re also increasing accessibility for remote patient monitoring. Especially for chronic conditions, remote patient monitoring is collecting continuous data, aggregating it, and sending it to care providers.
Real-World Examples of AI in Medical Diagnosis in Healthcare
AI is already helping providers with medical diagnoses in multiple ways. From decision-making engines to clinical data clouds, here are a few real-world examples:
Plat.AI
Plat.AI is a real-time decision-making engine that easily integrates AI into any existing platform or system with zero coding required. It speeds up data analysis, provides actionable insights, and improves data clarity, all while remaining secure and compliant.
Care Angel
A virtual nurse assistant, Care Angel, helps close the care gap for patients with chronic conditions who need continuous remote care. It also helps with medication management, addiction, pre and post-hospital discharge, and more through voice and text messages.
elluminate® IQ | eClinical Solutions
elluminate IQ is centralizing patient data, improving the efficiency of clinical trials. Currently leveraged by 100+ biopharmaceutical companies, elluminate IQ uses automation, AI, and analytics to aggregate patient data.
The Potential of Machine Learning in Healthcare
A specific branch of AI is machine learning, which uses and develops computer systems that learn using algorithms and models, not explicit instruction. Machine learning has a variety of specific use cases in healthcare, such as improved accuracy in diagnostic results, cost and time savings, and, most importantly, improved patient outcomes.
For example, machine learning can be used to schedule patient appointments, manage records, and automate repetitive tasks through a tool like Tebra. SubtleMR reduces noise in MRI scans, creating higher-quality images that improve care and reduce the patient’s time in the office. Insitro builds incredible predictive models from huge data sets, using machine learning to identify trends and help doctors prescribe more accurate medication.
The Next Decade of AI in Healthcare
AI is ready to have a significant impact on healthcare; already, AI predictive models are becoming more accurate in identifying risk factors for heart attacks and more accurate in helping radiologists diagnose cancer. AI chatbots provide on-demand, 24/7 advanced support, delivering patient recommendations when human doctors are off the clock. More providers are poised to adopt these technologies as the cost and resource savings continue to improve and patients become more accustomed to AI-supported healthcare.
How Ambiq Contributes
Many of these revolutionary AI-powered healthcare tools run with endpoint devices like wearable technology. Ambiq’s System-on-Chips (SoC) solutions use ultra-low power to optimize and extend battery life. Ambiq’s innovative technology is already at the heart of millions of AI-enabled healthcare tools worldwide and will continue to support these advances in healthcare technology.
Charlene Wan
