How is AI and Machine Learning Used in Healthcare? 5 Transformative Applications

All the advancements we’re witnessing in AI and Machine Learning in healthcare are not sudden breakthroughs. They're the result of the ongoing digital transformation in the healthcare industry over the last 10-15 years.

Healthcare has always been a driver of human technological progress. As humans, we naturally want to live long and healthy lives. So it’s almost inevitable that healthcare is one of the first areas of AI and ML application.

But, that wouldn’t be possible without data availability. In the past, healthcare companies have been treasuring all their data. And only recently we've started seeing the first big data sets becoming available. The data richness in this field has made it perfect for AI and Machine Learning technologies to gain traction. And to an industry that’s already scientific and has the habit of analyzing data in a statistically sound way, AI and Machine Learning are simply music.

The main goal of healthcare, pharma, and medtech companies is to treat patients better. So if we take a step back, we can see that any area that AI & ML models would impact, connects to patient care, from prevention and diagnosis to drug development and bedside care.

Let’s take autoinjectors as an example. A lot of data can be collected on these systems, such as the timing of treatment, the patient’s health status at that moment, and the dosage of the medicine administered. Companies can leverage this data to improve their products and how they treat and serve patients.

There are multiple areas where AI and ML can already make a difference. Let's jump into 5 transformative applications together.

Detecting diseases in their early stages buys everyone time: time to cure and time to live.

There are over 7000 rare diseases in the world and it takes an average of 4.8 years for rare patients to receive an accurate diagnosis. From automatically detecting suspected seizures to early-stage diagnosis of macular degeneration, AI/ML-enabled medical devices have become advanced enough to support healthcare professionals in accurately recognizing diseases and speeding up this time.

In 2023, the FDA approved 150+ medical devices that incorporate AI/ML technologies, which indicates the steady growth of AI and Machine Learning in healthcare. About 79% of these devices are in Radiology, as it’s also one of the most advanced areas of AI & ML applications. But we can see breakthroughs in the Cardiovascular area too.

FDA

Let’s take for instance the ECG-AI-approved algorithms, such as the recent FDA breakthrough designation of the ECG-AI algorithm for early identification of amyloidosis. Cardiac amyloidosis, a frequently undiagnosed condition leading to heart failure, is hard to detect early due to its rarity and non-specific symptoms, often missed in standard ECG interpretations. AI algorithms can pick up subtle signals that go undetected in a standard ECG and bypass human interpretation, obtaining 84.5% sensitivity and 83.6% specificity.

With diagnosis involving monitoring body signals, using imaging techniques, and conducting medical tests to analyze body fluid compositions and genomic screenings, AI becomes a great facilitator in the analysis of such data.

Artificial intelligence in healthcare has many benefits. It can provide accurate evaluations, reduce workload, decrease errors, and improve prediction and detection performance. In resource-restricted areas, AI & ML technologies are particularly helpful. When coupled with affordable diagnostic tools such as ECGs or wearables, AI/ML can democratize health access, allowing for early detection and improved treatment access in underserved areas.

By processing and analyzing large volumes of patient data from multiple sources, such as Electronic Health Records, wearables, healthcare applications, and medical devices (i.e insulin pens), AI models can predict and prevent episodes in chronic diseases like diabetes, heart disease, and asthma, leading to better management strategies.

Let’s take for instance diabetes. Around 537 million adults, ages 20-79 years, are living with diabetes worldwide. That’s 1 in 10. And the cases are expected to jump to 1.3 billion by 2050.

The most common types of diabetes are Type 1 - insufficient insulin production and Type 2 - insulin resistance. While they have distinct causes and implications, they both require:

• Constant monitoring

• A healthy lifestyle routine

• Strict medication management

• Regular check-ups

All these areas need the patient's commitment and discipline. However, poor medication adherence is growing by the day, patients are reluctant to use new apps to monitor their blood sugar, and lifestyle adjustments often come in last.

AI-powered tools, such as AI-driven glucose monitoring systems, aid in continuous monitoring and management of blood sugar levels. Connected to an AI-powered app, it offers personalized treatment plans based on individual patient data. AI can identify the patient’s patterns, detect early signs of complications, and offer preventive measures, by assisting healthcare professionals in finding the best treatment plans. This personalization not only improves the quality of life for patients but also prevents complications associated with diabetes or any chronic illness.

The integration of Artificial Intelligence and ML in healthcare is also reshaping how medical care is delivered. AI is at the forefront of advancing medical technology, such as the development of perfectly fitted prosthetics, using AI-powered software to design digital 3D models of patients' teeth, and enhancing the effectiveness of autoinjectors.

AI's ability to analyze data has made autoinjectors smarter. It can accurately determine the required pressure and drug dosage. For conditions like multiple sclerosis, rheumatoid arthritis, and diabetes, where medication adherence is crucial, these advancements could prove life-saving.

In the case of neurological disorders like Multiple Sclerosis, where disease-modifying therapies (DMTs) are some of the standard treatments, adhering to these therapies can prevent permanent disability and control the frequency of relapses. However, patients often miss their treatment, mostly due to injection-related issues. So it's not surprising that patients who always used an autoinjector had higher treatment adherence rates (79%), compared to those using a prefilled syringe (71%).

Equipped with AI and Machine Learning technologies, these autoinjectors would continuously learn from a patient's data, personalize drug dosing, and monitor the patient's adherence to the treatment.

This is particularly crucial in light of outpatient research indicating that over 50% of patients do not adhere to proper medication administration and dosing. A higher level of accuracy in treatment planning and execution offered by these AI-enhanced devices could dramatically improve patient outcomes.

The collaboration between medtech, pharma, and healthcare professionals in this arena opens up exciting possibilities for tackling issues related to accurate, personalized, and effective care delivery.

A patient’s experience doesn’t start at the healthcare provider’s doorstep and doesn’t end when they leave the facility. It’s an ongoing interaction defined by multiple factors, such as quality of care, communication and information, convenience, waiting time, privacy, and the digital experience.

AHRQ

As AI and ML continue to advance in healthcare, we’re seeing more transformative solutions that improve patient experience. One of them is the AI assistant that relies on both integration with medtech devices and Generative AI, functioning like a personal nurse. It continuously monitors the patient's health status, including vital signs, medication adherence, and recovery progress, which ensures timely interventions and adjustments in the treatment plan.

These AI algorithms can assist healthcare professionals in making real-time decisions at the patient's bedside. Like the AI-powered cardiac monitoring system does in the Heart Rhythm Center at Cedars-Sinai’s Smidt Heart Institute. Every morning it reports the patients’ heart rhythm data collected from their pacemakers and defibrillators overnight, alerting them to potential critical irregularities, before they become apparent to the patient or the nurses.

This kind of AI-driven care positively impacts the patient experience by reducing the need for constant check-ups, without interrupting patients' rest and at the same time alleviating unnecessary worries about their health.

Product development is a resource-intensive process, with every iteration consuming time and money. A lot like the Pac-Man game, navigating through various paths while avoiding unforeseen challenges, all in pursuit of the optimal solution.

The evolution of Generative AI in the healthcare space changes that.

Generative product design, especially in medtech, can accelerate the R&D process, allowing for rapid prototyping and iteration, and in the end, faster innovation cycles. This includes the use of 3D printing for faster physical iterations, enhancing the efficiency and effectiveness of the design process.

Springer

Creating new medical devices with generative design software, such as nTopology, allows engineers in medtech to come up with ideas faster, create virtual simulations, and evaluate the products’ performance, safety, and ease of use without having to physically manufacture them.

Generative design algorithms can analyze vast amounts of patient data and patterns, while factoring in optimization parameters and manufacturing limitations to create highly customized devices, leading to personalized treatments and shorter R&D cycles.

1. Public Perception: “AI will Replace Healthcare Professionals”

Many healthcare professionals are concerned that AI and machine learning will lead to complete automation that will replace their jobs. However, as discussed in our analysis, AI and ML should be viewed only as powerful augmentations of human capabilities. These technologies enhance the essential human elements in healthcare. Algorithms alone cannot heal – the chemical and physical aspects of care are irreplaceable. The integration of AI and ML aims to support healthcare professionals in delivering better care and developing better products for patients.

2. Regulatory and Ethical Considerations: “Adopting AI & ML is too Complicated from a Regulatory Standpoint”

Along with their benefits, AI & ML algorithms bring a few ethical and regulatory concerns that prevent them from becoming accurate decision making tools. If they’re not trained with representative data, their diagnosis and predictions can be subject to biases. At the same time, most current regulations are not aligned with the new tech, which impedes healthcare companies to access new data that can improve AI algorithms for better outcomes.

However, the examples of the companies that implemented AI & ML have shown the opposite. For instance, an interesting phenomenon is happening in the Real-World Data and Real-World Evidence space. The reliability of these analyses (RWE, RWD) have increased over time, as pharma companies have been using RWE and RWD to cut down clinical trial development costs and have a more efficient process. But these analyses are not regulated yet. However, regulatory bodies, such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) started to accept them, which has a big impact on improving the efficiency of clinical development for both pharma and medtech.

FDA

3. Integration Challenges: “Health Systems are Not Ready”

Patient Adoption

Understanding patient adoption is key for healthcare companies. Patients can sometimes struggle to follow prescribed treatment plans. It's a common human experience. Introducing a new health-monitoring app, therefore, might not be immediately accepted. This hesitation is natural.

Just as we adapt to change, patients too must go through their adjustment process. Recognizing this shared journey towards health management can help in designing innovative solutions that are also empathetic and user-friendly, creating a deeper connection with patient needs.

Infrastructure and Data ​Interoperability

Data interoperability in healthcare is a major factor for the effective implementation of AI and ML as it involves the integration of complex and diverse data from various sources like patient records, imaging systems, labs, medical devices, and wearables.

Nature

However many healthcare organizations rely on legacy systems that were not designed for the level of integration required for AI applications. This leads to difficulties in:

• Data exchange and consolidation

• Compatibility and connectivity between different systems and data formats

Apart from the challenges related to legacy systems, companies also face data utilization issues related to data quality and privacy.

AI algorithms rely on high-quality data for accurate predictions, yet medical data often faces issues like fragmentation and poor labeling. Current standards and legal frameworks generally handle data privacy adequately. However, once medical systems start communicating, sensitive patient info could be exposed.

Enhancing data quality and labeling, alongside ensuring syntactic (structure) and semantic (language) interoperability with privacy-preserving techniques, can improve medical decision-making and safeguard patient privacy.

As many healthcare companies lack the infrastructure to handle Data & AI initiatives, understanding current technical capabilities and identifying what adaptations are necessary for AI integration is not just prudent; it's a reliable indicator of the potential success of AI initiatives.

How can healthcare leaders better prepare their teams for the integration of AI technologies?

• Gain strong executive endorsement

• Accept it's not a three-month project, but a long-term commitment

• Recognize the massive competitive disadvantages of not acting on it

For medtech companies, the journey with AI and Machine Learning offers both immediate and long-term rewards. But the quick wins are essential, especially for companies venturing into 'moonshot' initiatives, as they provide tangible results that demonstrate value early on. It's about striking a balance: harnessing these immediate benefits while steadily working towards more ambitious, long-term objectives.

The key to bridging these goals lies in robust infrastructure and effective change management, cultivating a data-centric mindset throughout the organization.

Consider how this mirrors the patient experience. At first, patients appreciate the simplest benefits – like effortlessly booking appointments online or accessing preliminary examination results. Over time, without even realizing it, patients find these conveniences becoming an essential part of their prevention and care process. This seamless integration, a product of successful change management, plays a big role in this.

In the end, it's a collaborative journey - a unified effort towards better health outcomes.

So, are you actively shaping this transformation or finding yourself witnessing it from the sidelines?