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AI/ML HealthTech

AI/ML in Healthcare – Is It Real? And Is It Healthy?

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Written by Steve Zeller, Vice President of Product Marketing, Logicworks


We’ve all heard of Artificial Intelligence and Machine Learning (AI/ML) entering the field of HealthTech. Many of us immediately think about horrible outcomes like getting denied coverage by a bot, or receiving the wrong treatment from an automated doctor – But what is the true impact of AI/ML on HealthTech? And, most importantly, is it making us more or less healthy?


The field of HealthTech includes all of the supporting technology that helps medical professionals provide care. There are thousands of platforms used by doctors, nurses, researchers, and insurance providers that you probably haven’t even heard of, but may already be greatly impacting you. 


Your primary care physician, as well as any of your specialists, use Electronic Medical Records (EMR) systems to store, edit, and share your files. These EMRs are connected to each other and to your insurance company through a Health Information Exchange (HIE). HIE’s contain millions of health records from all over the country. Once your records are in an HIE, they are de-identified (your name and confidential details are removed) and they are entered into a data warehouse. These data warehouses feed your anonymized data into tools like Population Health and Predictive Analytics systems with the goal of aggregating health information to predict trends and improve outcomes across the general public. 


Hospitals also generate substantial amounts of data from various devices used in diagnostic imaging scans, including X-rays, MRIs, CT/PET scans, cardiology screening, and retinal scans. Software tools analyze these digital images, and Picture Archiving and Communication Systems (PACS) store and manage them.


Consequently, a wealth of digital data regarding your medical history, as well as details about your eyes, ears, heart, brain, and other organs, now resides in the cloud. The key question is: How is this data being utilized, and does it genuinely contribute to our well-being? Let’s examine real-world examples.


AI in Eye Disease – Google DeepMind

DeepMind was founded in 2010 by Demis Hassabis, Shane Legg, and Mustafa Suleyman, with the goal of developing AI algorithms capable of general-purpose learning and problem-solving. It gained global recognition when its AI program, AlphaGo, defeated the world champion Go player in 2016. Since then, DeepMind has focused its efforts on the potential applications of AI in various domains, including healthcare, to address real-world challenges and make a positive impact on society. They have developed AI algorithms for medical imaging analysis, with a particular focus on eye diseases. DeepMind’s AI models have been trained on large datasets of retinal images to accurately detect and classify conditions like diabetic retinopathy and age-related macular degeneration. These algorithms have demonstrated impressive performance and have the potential to assist healthcare professionals in making more accurate diagnoses. DeepMind also collaborates on projects related to clinical decision support, patient monitoring, and healthcare operational improvements. 


AI in Drug Development – BenevolentAI

BenevolentAI leverages artificial intelligence (AI) and machine learning (ML) to accelerate drug discovery and development. Founded in 2013, their platform analyzes vast scientific datasets to identify potential drug targets, predict drug efficacy, and optimize properties. By harnessing AI/ML algorithms, BenevolentAI aims to shorten the time and reduce the cost traditionally associated with the drug discovery process. They also explore applications in precision medicine, using patient data to identify subpopulations that may respond better to specific treatments. Through collaborations with pharmaceutical companies and academic institutions, BenevolentAI strives to drive innovation and improve patient outcomes.


AI in Population Health – PhysIQ

PhysIQ’s technology aims to improve patient outcomes by providing real-time patient monitoring, predictive analytics, and personalized insights. Their AI platform continuously monitors and analyzes subtle changes in vital signs, such as heart rate, respiratory rate, and activity levels, enabling early detection of health deterioration and proactive interventions. Their technology has been used in clinical trials to remotely monitor patients, enhance trial efficiency, and capture more comprehensive data. Additionally, PhysIQ’s platform has the potential to support remote patient monitoring programs, post-acute care, and chronic disease management, empowering healthcare providers to deliver proactive and personalized care while reducing healthcare costs. With a focus on continuous physiological analytics, PhysIQ is at the forefront of transforming healthcare delivery through AI and advanced analytics.


AI Dangers, Discrimination, and Missteps

While most of our research uncovered positive outcomes from AI in healthcare, there have been some concerns. A recent study published in Science found a widely used commercial AI algorithm for predicting healthcare needs underestimated the needs of black patients compared to white patients. 


The use of AI chatbots for mental health support has also raised red flags regarding privacy and data security. In 2018, a mental health chatbot called Woebot inadvertently exposed sensitive user data due to a software bug, highlighting the importance of robust security measures in AI-driven healthcare services.


A study published in the Journal of the American Medical Association highlighted that an AI algorithm designed to predict acute kidney injury in hospitalized patients showed subpar performance when tested in external healthcare systems, demonstrating the challenges of generalizability and the need for careful validation in different contexts.


AI’s Future in HealthTech

So what’s next? AI/ML could provide the foundation for personalized medicine, early disease prevention, rapid drug and vaccine development, virtual assistants, and perhaps even, though a little scary, robotics-assisted surgery. (To be clear, surgeons perform robotic surgery very commonly today, but AI is not generally contributing.) These advancements can lead to improved patient outcomes, timely interventions, more precise treatments, and greater accessibility to healthcare services. However, it is crucial to address ethical considerations, and regulatory challenges, and ensure responsible implementation to maintain patient trust and ensure the benefits of AI in healthcare are realized.


Where Does Logicworks Come In?

As a leading migration, DevOps, and cloud computing consultant, Logicworks enables HealthTech companies to take advantage of AI/ML tools by migrating all of their data to a cloud platform like AWS or Microsoft Azure, centralizing analytics in a Data Warehouse / Data Lake system, and automating their software development and operations processes. Check out our AWS Accelerators to learn how your company can quickly move from a legacy database platform to a cloud-native system like Amazon Aurora! 


Learn more about how Logicworks can help you with your cloud strategy and ongoing support.

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