Artificial Intelligence (AI) has emerged as a world-changing technology with a wide range of applications across industries. From virtual assistants to autonomous vehicles and advanced data analytics, AI has started to revolutionize the way we live and work. One of the sectors that is rapidly developing because of the emergence of AI is medical technology (MedTech). The global market of MedTech is expected to reach $570 billion in revenue by the end of this year, with medical devices accounting for the largest share of this: $470 billion. These devices are increasingly powered by artificially intelligent systems.
What is Artificial Intelligence?
AI refers to computer systems that can perform tasks that typically require human intelligence. It involves creating algorithms and models that enable machines to understand, reason, learn, and make decisions based on data.
Machine learning (ML) is an important part of AI, where algorithms are designed to learn from data and improve their performance over time without explicit programming. Through training on large datasets, ML models can recognize complex patterns and make accurate predictions or decisions.
What is Medical Technology?
Medical technology, abbreviated to MedTech, refers to any piece of technology that is used in healthcare or medical research. This encompasses a wide range of technologies and combines knowledge from medicine and engineering to drive innovation. Medical technology plays a crucial role in modern healthcare systems, enabling healthcare professionals to deliver more accurate diagnoses, provide effective treatments, and enhance patient outcomes. Examples include, but are not limited to:
- Virtual health assistants
- Laboratory / Imaging related analyses
- Robotic support during surgery
- Wearables (i.e. prosthetics)
- Remote monitoring of patients
How can AI impact MedTech?
The medical industry generates and uses various types of data that are essential for patient care, research, and decision-making. These data types include:
- Electronic Health Records (EHR): these contain comprehensive patient information, including medical history, diagnoses, medications, allergies, and laboratory test results.
- Medical imaging data: medical imaging techniques, such as X-rays, CT scans, and MRI scans, generate visual representations of internal body structures.
- Genomic data: Genomic data refers to information about an individual’s genetic makeup.
- Wearable and sensor data: with the rise of wearable devices and sensors, healthcare professionals can collect real-time data on patients’ physiological status, such as heart rate and blood pressure.
- Clinical trial data: clinical trials generate data on the safety and efficacy of new treatments or interventions.
- Patient-reported data: patient-reported data includes information provided directly by patients about their symptoms.
Each type of data in the medical field offers unique insights and presents its own challenges in terms of collection, storage, security, and analysis. Integrating these data into medical products using AI and analyzing these diverse data sources using AI can give rise to valuable knowledge and improve healthcare outcomes.
Development of new AI-powered applications
AI can enhance the entire medtech product development lifecycle, from ideation and design to manufacturing. It has the potential to accelerate innovation, improve patient outcomes, and drive advancements in medical technology in many different ways.
When you combine AI and healthcare, interesting questions arise about explainability and responsibility. Often, ML algorithms function as a sort of black box wherein the exact reasons for the decisions made by the algorithm are unclear. This lack of explainability reinforces the importance of having other methods to ensure the reliability of the algorithms. This, along with many other points which we dove into in a separate article, continue to make it difficult to adopt AI into clinical practice.
Machine Learning applied in healthcare
That doesn’t mean that there isn’t a rapid growth of AI in healthcare. In the following sections, we will explore some applications of ML in healthcare and how they’re transforming the industry.
AI-powered virtual assistants
You have probably already encountered some of these yourself; one of the more prominent applications of ML is the AI-powered virtual assistant. These virtual assistants are designed to mimic human conversation and provide personalized healthcare guidance and support. These assistants are also widely applied in healthcare.
Virtual assistants are designed to mimic human conversation and provide personalized healthcare guidance and support.
By leveraging natural language processing and ML algorithms, these assistants can understand patients’ queries, provide information about symptoms, suggest potential diagnoses, and offer guidance on treatment options. They can function as an initial judge that determines if a medical professional needs to be seen at all and if so, how urgently. AI-powered virtual assistants have the potential to improve patient engagement, enhance access to healthcare information, and ease the burden on healthcare professionals.
ML in medical devices
We already mentioned that medical devices contribute to the majority of revenue in medical technology. These devices can leverage ML algorithms to monitor patients’ vital signs, detect abnormalities, and deliver real-time feedback. For example, wearable devices equipped with sensors and ML capabilities can continuously monitor heart rate, blood pressure, and other physiological parameters, alerting healthcare providers in case of any deviations from the normal range. This enables early detection of potential health issues and timely interventions, improving patient outcomes and reducing healthcare costs.
For example, Google DeepMind has developed a smartphone app called Hark that helps hospital staff identify the needs of a patient in hospital. It works by smartly combining large volumes of patient data like medical records, actions taken by doctors and nurses and results of tests.
ML in medical imaging
Medical imaging plays an important role in diagnosing and monitoring various medical conditions. ML algorithms have shown the remarkable capability of aiding the analysis and interpretation of medical images from devices such as X-rays, MRI scans, and CT scans. By training on large datasets, these algorithms can learn to recognize patterns and anomalies in medical images, aiding radiologists in accurate diagnosis and reducing the risk of human error. Moreover, ML algorithms can assist in the early detection of diseases, such as cancer, by identifying subtle changes in imaging data that may not be noticeable to the human eye. Recently, a Swedish study found that AI is actually better at detecting breast cancer than radiologists. On UbiOps, Ellogon.AI is using AI on medical images to determine the need for immunotherapy in patients.
How AI is applied in medical research
ML and AI are also transforming the landscape of medical research. Researchers can use ML algorithms to analyze large amounts of biomedical data. These algorithms can identify patterns, correlations, and insights that may contribute to the development of new treatments and optimization of existing therapies. By automating data analysis tasks, ML accelerates the pace of research and enables researchers to make data-driven decisions more efficiently. In the following sections, we will explore some of the applications of ML in medical research.
ML in drug discovery
Traditionally, developing new drugs is a time-consuming endeavor. However, ML algorithms can significantly speed up this process by analyzing data related to molecular structures, biological interactions, and pharmacological properties. By predicting the effectiveness and safety profiles of potential drugs, ML algorithms can help researchers identify promising molecules for further investigation. Additionally, ML models can aid in optimizing drug dosages, predicting adverse reactions, and identifying patient populations that are most likely to benefit from specific medications. This application of ML has the potential to accelerate the discovery of new treatments and improve patient outcomes.
ML in fundamental research
ML enables the integration of diverse types of data, such as genomics, proteomics, and metabolomics, leading to a holistic understanding of diseases and biological mechanisms. This approach leads to the discovery of novel biomarkers, identification of diseases, and better understanding of complex biological networks. In addition, ML in fundamental research does not only enhance our understanding of diseases. It also paves the way for the development of personalized medicine.
One famous example of AI applied in fundamental research is DeepMind’s AlphaFold. It’s able to predict the spatial structure of proteins from their amino acid sequences. This is important, because it helps researchers to more effectively target certain protein sites which aids drug development and helps gain biological insights that are difficult to discover experimentally.
Running AI applications for healthcare on UbiOps
UbiOps is a Machine Learning Operations (MLOps) tool specializing in deployment and serving, focused on allowing you to run your AI solutions at scale, with ease. UbiOps is secure, completely cloud agnostic and can run in the cloud as well as on-premise. UbiOps is also suited for hybrid cloud setups. Do you want to start scaling your medical ML solution, but you’re limited by your capacity or ability to manage all the IT hassle that comes along with that? Then UbiOps can help you out and make sure that you can focus on quick iteration and model development. For example, take a look at what we’ve achieved together with Ellogon.AI! They use big data and AI on UbiOps to efficiently determine whether a cancer patient may benefit from immunotherapy.
Are you interested in how that works? Check out our documentation or book a demo with us!