Artificial Intelligence in medical devices

Discover the current state of AI in medical devices, its benefits, and future trends
Tue 15 Dec 2020

More and more medical devices are using Artificial Intelligence (AI) to improve patient diagnostics and to treat patients more effectively. The reason is that AI has become an essential key to make sense of the ever-increasing data generated by medical devices. AI can analyze large volumes of complex data in novel ways, discover new relationships in the data, learn from the data, and automatically improve its performance with ‘experience’. Medical device users and producers can enjoy new functionalities, new ways of managing doctor-patient relationships, and improve healthcare delivery.

Let’s look at how artificial intelligence is powering medical devices, some examples of AI applications, and what are the challenges and opportunities that emerge because of AI.

An overview of AI in medical devices

The place of artificial intelligence in medical devices is still slightly fuzzy as it has recently seen major changes and advancements. So let’s firstly start by defining the term medical devices, and how are the AI-based health technologies classified.

Medical devices

Medical device is any instrument, apparatus, implement, machine, appliance, implant, reagent for in vitro use, software, material or other similar or related article, intended by the manufacturer to be used, alone or in combination, for human beings, for one or more of the specific medical purpose(s).

Data generated by medical devices

Some medical devices generate:

  • digital signals (ECGs, EEGs, blood pressure signals, ultrasound, hearing aid signals, etc.)

  • other kinds of outputs such as images (scans, pictures, IVD data, etc.).

The quantity of such data is increasing at a fast pace, notably due to the fast development of remote health monitoring.

Some non-digital medical devices can also generate data when being monitored and observed in their use: visual observation and scans of the evolution of a prosthesis over time, visual observations of the evolution of a spine device over time, etc.

The impact of AI on medical devices

One may have noticed that the large tech companies have been accelerating in developing smart products, such as smart wearables. Many of them are using AI and developing new AI applications to bring new, innovative, patient-friendly functionalities.

Beyond large tech companies, AI in medical devices is clearly accelerating, in Europe like elsewhere. AI is actually opening new doors to the medical devices industry by giving medical device and equipment manufacturers the possibility to:

  • Use the data they collect in novel ways, with no limits in processing speed or volume;

  • Find hidden correlations in their data, sometimes in real-time;

  • Generate new ways of helping patients and developing new, sometimes unique products;

  • Reach new customer segments.

AI solutions as medical devices

Whereas the regulatory definition of a medical device was previously rather narrow, AI-based solutions with a medical purpose have recently become medical devices as such. The European Union actually issued the Regulation EU 2017/745 on Medical Devices (Medical Devices Regulation) describing that software programs created with clear intention to be used for medical purposes are considered as medical devices. This broadening of the definition of what is a medical device affects products that are explicitly intended to prevent or monitor disease without having a diagnostic or therapeutic purpose. Therefore, AI-based health technologies that help to diagnose, predict, monitor, and prevent a disease can now be considered as medical devices.

How can AI be used by MedTech companies

AI for MedTech is a fascinating field where new applications are being developed almost every week. Typically, these are the ways in which AI is used by MedTech companies.

  • Diagnose: Lead to better and timely diagnosis of a medical condition. Example: diagnose eye pathologies.

  • Prevent: Predict pathologies and enable the caregiver to take a timely decision. Example: detecting early signs of blood cancer;

  • Care: Help automate follow-up of patients even in a remote setting. Example: remote monitoring of elderly patients to prevent risks of injuries.

  • Personalize: Personalize the treatment of each individual patient. Example: individual prediction of the risk of developing Atrial Fibrillation.

Beyond these uses, Artificial intelligence can also:

  • Help improve the quality of medical data so they can be used for predictive analytics. Example: augmentation of medical images so they can be better understood by an AI algorithm.

  • Improve the operational efficiency of care institutions. Example: using predictive maintenance to maintain medical equipment on time.

The future of AI in the medical device industry


Of course, the implementation of Artificial intelligence in the MedTech industry still has some challenges to overcome.

  • Data incompleteness: Medical data can have problems such as inconsistency and/or incompleteness, like for example data generated from electronic health record systems. This challenge was particularly evident in a study done on the survival of pancreatic patients using data extracted from Columbia University Medical Center’s EHR in the past decade. The study showed that 52% percent of the patients did not have the information on the stage of their disease, such as tumor size. For a successful implementation of AI for medical devices, it is important that the data used is complete and accurate. Luckily, AI can also help with data preparation and can improve the quality of the medical data.

  • Legal and ethical concerns: With the rise of AI-based software, some legal and ethical concerns have started to emerge. More specifically, the question under which circumstances (if at all) the principles of informed patient consent should be deployed. To what extent do clinicians have the responsibility to educate the patient around the form of machine learning used by the system, the kind of data it inputs and gathers. Particularly, the question of handling patient’s data for AI/ML-based SaMD has been an ongoing debate in the European Union and the United States.

  • Need for safety and transparency: Safety is one of the biggest challenges of AI in healthcare. Considering the complexity of how AI algorithms work, it is important to ensure that AI is safe and effective. Reassuring health professionals to take a turn towards AI can lead to more trust in AI-based decisions. For example, understanding the basics of the AI software, the output results, its usefulness, and how to interact with the software. Moreover, AI developers should be sufficiently transparent, for example about the kind of data used and if there is any risk of possible unlawful biases and prejudicial elements of the AI decision-making.


In a future medical device industry powered by AI, some significant opportunities will arise:

  • Towards augmented users and clinicians: AI is now helping clinicians and patients by “augmenting” them, i.e making them better informed and better equipped through smart insights. This is why the demand for AI in healthcare comes from two sides: on one hand, care providers and healthcare professionals see more and more opportunities from AI. On the other hand, there is an increasing demand from patients to better manage their health remotely.

  • Saving lives: According to a report from Deloitte and MedTech Europe, around 400,000 lives can be potentially saved annually through AI.

  • Saving time and financial resources: AI in medical devices could help save up to €200 billion annually, and reduce the duration of certain medical tasks up to 1,8 billion hours less every year. That way medical professionals could make better use of their time, for example, doctors seeing more patients instead of working on the health records.

  • Improving patient care: From prevention and early detection to diagnosis, treatment, and care management - AI can help improve each stage in the patient journey.

  • Smarter medical devices: A recent survey showed that 82% of MedTech leaders consider AI important to their companies. Because of the potential for medical device performance to be significantly improved through AI, we can expect to see more and more devices that incorporate machine learning to appear on the healthcare market. AI can (without any doubt) make medical devices more reliable, accurate, and more automated.

  • Healthcare to everyone: AI-based SaMD have a significant potential to bridge the gap between access, affordability, and effectiveness in healthcare. For example, smartphone medical devices that use AI to diagnose a medical condition can allow for more affordable healthcare for everyone, at any time from anywhere. In the future, we can expect to see AI to continue to expand its applicability to medical devices, for example, medical devices integrating AI together with virtual reality.


Over the past decade, artificial intelligence has opened a whole new spectrum of diagnostic and therapeutic possibilities for patients. By powering a new generation of systems that equip clinicians with smart tools when delivering care, AI will lead the way in a new era of exciting breakthroughs in patient care.

Kantify helps companies succeed in their AI journey. We have developed an expertise in helping medical device companies use AI and improve patient care. Let’s get in touch to discuss your challenge in more detail!