Medical devices with artificial intelligence: get through audits and licensing with confidence Neither the EU directives and regulations (e.g. Luckily, AI can also help with data preparation and can improve the quality of the medical data. The emergence of Artificial Intelligence (AI), including Machine Learning (ML), has identified a challenging new front for the regulation of medical devices. MDD, MDR) nor the harmonised standards (e.g. The place of artificial intelligence in medical devices is still slightly fuzzy as it has recently seen major changes and advancements. ARTIFICIAL INTELLIGENCE IN THE MEDICAL DEVICE INDUSTRY Posted by Brian Hess on March 27, 2019 Artificial intelligence (AI) systems are designed to simulate human thinking capabilities in order to facilitate complex or repetitive tasks, often providing detailed new insights and allowing users to focus on other aspects of operations. In this example, a Chihuahua and a muffin (source) (click to enlarge). However, it observes that previously approved medical devices based on AI procedures worked with “locked algorithms”. Therefore, it looks at the objectives of a modification to the algorithm and distinguishes between: The FDA wants to use these objectives to decide on the need for new submissions. The study showed that 52% percent of the patients did not have the information on the stage of their disease, such as tumor size. The data are visualized here as a heat map (source). Fig. According to GlobalData forecasts, the market for artificial intelligence (AI)/machine learning (ML) platforms will reach $52B in 2024, up from $29B in 2019. Although a lot of devices have already been approved (e.g. Artificial Intelligence (AI) in Healthcare! Therefore, the Johner Institute is developing such a guideline together with a notified body. Consultation Medical device users and producers can enjoy new functionalities, new ways of managing doctor-patient relationships, and improve healthcare delivery. 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. The manufacturer plans to change the algorithm, for example to reduce false alarms. 4a: Algorithm Change Protocol (ACP) from the FDA's proposed regulatory framework for software that use machine learning (click to enlarge), Fig. The process approach is also in the foreground. Typically, these are the ways in which AI is used by MedTech companies. The assumption that artificial intelligence in medicine mainly uses neural networks is not correct. Improve the operational efficiency of care institutions. requests: Person Responsible for Regulatory Compliance, Glossary for medical device manufacturers, In Vitro Diagnostic Medical Device Performance Evaluation, Arkerdar: Business Intelligence for Business, Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD), clinical evaluation according to MEDDEV 2.7.1 Revision 4, “Interpretable Machine Learning” by Christoph Molnar, guideline for the safe development and use of artificial intelligence. 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. Healthcare to everyone: AI-based SaMD have a significant potential to bridge the gap between access, affordability, and effectiveness in healthcare. Watson versagt” [“Dr. I said I was afraid.”. We can no longer afford and no longer want to pay for medical staff to perform tasks that computers can do better and faster. 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. Artificial Intelligence in Medicine More and more medical devices are using artificial intelligence to diagnose patients more precisely and to treat them more effectively. Drues sees locking the machine learning algorithm is a Band-Aid solution — not a longterm fix. embodied AI, i.e. More and more medical devices use artificial intelligence (AI) and machine learning (ML) to perform or support medical applications. For devices that are used for diagnostics purposes, the sensitivity and specificity, for example, must be demonstrated. We developed this guideline with notified bodies, manufacturers and AI experts. EN IEC 62304) make concrete demands on medical devices which use artificial intelligence processes and machine learning in … The algorithm evaluates the pixels in the rising part of the digit as damaging for classification as "1". So let’s firstly start by defining the term medical devices, and how are the AI-based health technologies classified. The requirements of the guideline are grouped along these processes. Neural networks, deep learning, are part of machine learning. 3. 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. Need for safety and transparency: Safety is one of the biggest challenges of AI in healthcare. With many medical device manufacturers already investing in AI capabilities, it’s clear that the industry is devoted to enabling the technology within their products and services. The techniques are used for the purpose of classification or regression. In this blog we will try to clarify our understanding of what is meant by Artificial Intelligence (AI) by limiting the definition in … The reason is that AI has become an essential key to make sense of the ever-increasing data generated by medical devices. The FDA gives examples of when a manufacturer may change a software algorithm without asking it for approval. Artificial intelligence (AI), once little-known outside of academic circles and science fiction films, has become a household phrase. That way medical professionals could make better use of their time, for example, doctors seeing more patients instead of working on the health records. Artificial intelligence (AI) can detect significant data set interactions and is commonly used for the expectation of outcomes, treatment, and diagnosis in several clinical conditions. 1: Artificial intelligence is based on numerous techniques, of which machine learning is only one part. Example: detecting early signs of blood cancer; Care: Help automate follow-up of patients even in a remote setting. Internal and external auditors and notified bodies use the guideline to test the legal conformity of AI-based medical devices and the associated life-cycle process. However, it is poorly understood how and which AI/ML-based medical devices have been approved in the USA and Europe. It has to be expected that the media will write over-the-top and scandalized reports on cases where bad AI decisions have tragic consequences. These also include risks resulting from incorrect predictions made by sub-optimal models. One may have noticed that the large tech companies have been accelerating in developing smart products, such as smart wearables. “You already have examples of companies using medical VR devices, but the integration with AI and the real-time feedback (and following adaptation) is not straightforward. Whereas today mainly neural networks are in the spotlight, They take a shot at their very own without being encoded with directions. The FDA tries to explain, for the two types of algorithm modification, when: The new “framework” is based on well-known approaches: The FDA recognizes that, according to its own regulations, a self-learning or continuously-learning algorithm that is in use would need to be inspected and approved again. other kinds of outputs such as images (scans, pictures, IVD data, etc.). In 2019, the Johner Institute, together with notified bodies, published a guideline for the safe development and use of artificial intelligence - comparable to the IT Security Guideline. The free online book “Interpretable Machine Learning” by Christoph Molnar, who is one of the keynote speakers at Institute Day 2019, is particularly worth a read. Some medical devices use several methods at the same time. Regulatory consultant Mike Drues says he has had clients forced to dumb down their AI technology, with U.S. FDA requiring they lock the algorithm. 2). (click to enlarge). Most medical devices are 510 (k)s and may already have such potential, if substantially equivalent to a device that currently exists. Medical devices. Time-of-death prognosis for intensive care patients, Vital signs, laboratory values and other data in the patient's records, Table 1: Comparison of the tasks that can be performed with artificial intelligence and the data used for these tasks, Fig. More and more medical devices are using Artificial Intelligence (AI) to improve patient diagnostics and to treat patients more effectively. Other medical devices have the same opportunity, even if AI and ML are not used. components of or accessories for medical devices or in-vitro diagnostic medical devices that are or comprise AI, including AI sold as a service or as part of a hardware device (a.k.a. What gold standard did you use when labeling the training data? Beyond large tech companies, AI in medical devices is clearly accelerating, in Europe like elsewhere. This shows how important it is for the result that the training data is representative of the data that is to be classified later. 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. Reassuring health professionals to take a turn towards AI can lead to more trust in AI-based decisions. On the other hand, there is an increasing demand from patients to better manage their health remotely. Improving patient care: From prevention and early detection to diagnosis, treatment, and care management - AI can help improve each stage in the patient journey. With it, you can filter the requirements of the guideline, transfer it into your own specification document and adjust it to your specific situation. From diagnostic and imaging technologies to therapeutic applications and robotics, the potential for machine learning and AI technologies reaches almost every corner of the medtech world. The current research literature shows how manufacturers can explain and make transparent the functionality and "inner workings" of devices for users, authorities and notified bodies alike. The FDA considers there to be four pillars that manufacturers can use as a basis for ensuring the safety and benefit of their devices, including for modifications: Fig. The place of artificial intelligence in medical devices is still slightly fuzzy as it has recently seen major changes and advancements. 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). Kristopher Sturgis | May 17, 2018 Machine learning and artificial intelligence (AI) have long been heralded as the future of transformative technologies. The capability of a machine to imitate intelligent human behavior”, Detection, analysis and improvement of signals e.g. For example, using Layer Wise Relevance Propagation it is possible to recognize which input data (“feature”) was decisive for the algorithm, e.g. Approval process including the FDA's pre-cert program, de novo procedures, etc. AI for MedTech is a fascinating field where new applications are being developed almost every week. Worldwide interest in artificial intelligence (AI) applications is growing rapidly. For a successful implementation of AI for medical devices, it is important that the data used is complete and accurate. closed-loop medical devices (artificial pancreas, AED) AI has been introduced into most electronic medical record systems for a wide variety of tasks. Since the model was trained with a certain quantity of data, it can only make correct predictions for data coming from the same population. 1). Fig. We have developed an expertise in helping medical device companies use AI and improve patient care. The questions that auditors should ask manufacturers include, for example: How did you reach the assumption that your training data has no bias? We are facing a period of disillusionment. 4b: Decision tree the FDA uses to decide whether modifications to software based on machine learning make a re-approval necessary (click to enlarge). Artificial Intelligence has been broadly defined as the science and engineering of making intelligent machines, especially intelligent computer programs (McCarthy, 2007). The manufacturers must demonstrate the benefit and performance of the medical device. The Covid-19 pandemic has triggered a rapid implementation of new technologies in the medical technology industry. Which framework conditions must be observed? 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. Artificial intelligence is currently receiving a lot of hype. That trend will continue to expand as the public becomes more exposed to AI technology in everyday products, ranging from their cars and home appliances to wearable devices capable of tracking the metrics of their everyday routines. for classification. Before development, manufacturers must determine and ensure the competence of the people involved (, It does not expect a new submission, only the documentation of the modification by the manufacturer. So it is about machines ability to take on tasks or make decisions in a way that simulates human intelligence and behavior. by the FDA), a lot of regulatory questions remain unanswered. Use the Excel version of the guideline that is available here for free. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. When we were writing it, it was important to us to give the manufacturers and notified bodies precise test criteria to provide for a clear and undisputed assessment. More and more medical devices are using artificial intelligence to diagnose patients more precisely and to treat them more effectively. 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. Let's discover why. 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. An essential part of the work consists of collecting and processing the training data and using it to train the model. Artificial Intelligence has also enabled the design of smartphone software and wearable devices that transmit patients’ clinical data directly to a medical practitioner through a simple Wi-Fi connection. The Food & Drug Administration, or FDA in the United States, has decided to trust Artificial Intelligence and Machine Learning as medical devices. Artificial intelligence (AI) aims to mimic human cognitive functions. What requirements does the data have to meet in order to correctly classify your system or predict the results? Particularly if the machine starts to be superior to people, it becomes difficult to determine whether a physician, a group of “normal” physicians, or the world's best experts in a discipline are the reference. showed that support vector machines are used most frequently (see Fig. But that seems too strict even for the FDA. The questions are typically also discussed as part of the ISO 14971 risk management process and the clinical evaluation according to MEDDEV 2.7.1 Revision 4. for irradiation planning, Decision as to whether there is a diagnosis, Deciding whether cells are cancer cells or not. A lot of artificial intelligence techniques use machine learning, which is defined as follows: “A facet of AI that focuses on algorithms, allowing machines to learn and change without being programmed when exposed to new data.”, Source: Arkerdar: Business Intelligence for Business. We would like to see such specificity from the European legislators and authorities. Fig. The American agency announced on Tuesday 12 January this year a course of action in favour of AI and ML in the health field. On January 12, 2021, the US Food and Drug Administration (FDA) released its Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device … More specifically, the question under which circumstances (if at all) the principles of informed patient consent should be deployed. 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. Even manufacturers of medical devices with artificial intelligence are confronted with many uncertainties during development, approval and after marketing. 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. Legal and ethical concerns: With the rise of AI-based software, some legal and ethical concerns have started to emerge. 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Collecting, processing and “labeling” training data is time-consuming. Regulating Artificial Intelligence as a Medical Device Artificial Intelligence (AI) is quickly becoming an integral part of our daily lives—from immersive virtual reality video games to quick email reply suggestions, computers around us are becoming smarter and more contextually aware. And deep learning is, in turn, part of machine learning and is based on neural networks (see Fig. 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. Otherwise, the algorithm would only correctly predict the data it was trained with. Diagnosis of heart diseases, degenerative brain diseases, etc. They must ensure that the software has been developed in a way that ensures repeatability, reliability and performance (including MDR Annex I 17.1). So let’s firstly start by defining the term medical devices, and how are the AI-based health technologies classified. This makes sense, because with a "6" this area typically does not contain any pixels. Many of them are using AI and developing new AI applications to bring new, innovative, patient-friendly functionalities. The terms artificial intelligence (AI), machine learning and deep learning are often used imprecisely or even synonymously. Artificial Intelligence Medical Devices (AIMD) The purpose of this Work Item is to achieve a harmonized approach to the management of artificial intelligence (AI) medical devices. Have you validated systems that you are using to collect, prepare, and analyze data, and to train and validate your models? AI can be applied to various types of healthcare data (structured and unstructured). Guidelines, “Good Machine Learning Practices” as the FDA calls them, are still lacking. 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