How is AI Disrupting the Medical Device Industry: Top Trends, Use Cases, and Benefits
Life Sciences | 8 min READ
Emerging Trends in the Medical Device Industry
How is AI Catching Up Fast in Terms of Adoption?
When we think of technologies such as Artificial Intelligence (AI) and its close cousin, Machine Learning (ML), it is easy to dismiss those technologies as the products of ‘speculative science fiction, the real benefits of which will only be realized beyond our lifetimes. The reality, however, is starkly different. As the famed writer William Gibson has once put it, “The future is already here. - It’s just not very evenly distributed”. Today, both AI and ML deliver incremental improvements and become that much closer to being implemented in the way their creators had initially envisioned.
John Danese
John Danese

Industry Director

Life Sciences


For the medical device manufacturing industry, this opportunity was a long time coming. From providing enhanced telehealth capabilities to increasing patient adherence, AI-infused systems and technologies are driving a slew of changes for medical device manufacturers as they scramble to put together more resilient remote servicing models that are cheaper and more effective in disseminating therapeutic benefits to those in need. With nearly 77% of surveyed manufacturers deeming digital transformation to be a priority, it is no surprise that the medical device manufacturing industry is taking to these technologies with renewed vigor to differentiate the effectiveness and cost benefits of their therapeutic products, especially in the face of commoditization of specific medical devices, the trends driving value/outcomes-based healthcare and increased pricing pressure.
AI In Medical Device Manufacturing – Reasons to Adopt
Better Patient Outcomes
The research firm Frost & Sullivan estimates that AI can improve patient outcomes by 30% to 40% while reducing treatment costs by up to 50%. As physicians report high rates of burnout and patients demand more attentive care for increasingly complex issues, AI has proven to be a credible solution in easing the burden on physicians and enabling them to care for more people attentively and effectively. Increased patient adherence, enhanced clinical decision-making, and increased affordability of critical technologies such as MRI scans indicate how AI enables better patient outcomes.
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Improved Regulatory Control
Due to their capacity to affect patients’ health outcomes for better and for worse, medical devices are subject to strict regulatory controls that provide reasonable assurance of their safety and effectiveness. While preventing the supply of faulty appliances in the market, this level of authority comes with the flipside of stymying the adoption of novel technologies like AI in medicine and highly controlled and validated processes in medical device manufacturing. AI can expedite this process by drastically reducing inspection times by delivering more significant insights from subtle patterns in large data sets and advanced analytics into how systems function over time and across various scenarios.
Faster and Safer Diagnosis
Predicted by Forrester to be the second-largest AI software segment by 2025, AI-centric apps that aid in medical diagnosis is among the most promising and popular medical technology applications. This comes as a boon for manufacturers as they now have an entirely new well of growth to tap into. From early detection of breast cancer to assessing risks from myocardial diseases, AI-enabled systems can diagnose with speed and quality.
Proactively Identifying Safety Signals
Utilizing bio-electric markers and movement patterns to detect the well-being of patients is one of the hallmarks of AI-enabled systems. Shortly after its release, the Apple Watch saw it being used to save the lives of many of its users, who credited it for helping them survive sticky situations where they were all by themselves. As companies are on the verge of perfecting this technology and democratizing it, the only challenge lies in getting it to the patients, yet another opportunity for device manufacturers to seize.
Top Use Cases of AI In Medical Device Manufacturing
Top Use Cases of AI In Medical Device Manufacturing

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Top Use Cases of AI In Medical Device Manufacturing
Personalized Treatment
Ultimately, people’s bodies are very different, and generalized treatment methods can only work for basic ailments. For advanced diseases, patients require a bespoke treatment setup that considers the nuances of their bodies. Personalized Medicine (PM) is often the only recourse for patient groups that have failed traditional systems.
On this front, Artificial Intelligence, more specifically its sister technology, Machine Learning (ML), has proven to be of immense use as it can sift through massive volumes of anonymized patient data and glean patterns, correlations, and commonalities in patient populations that can point to the underlying causes of disease. This is a highly complicated statistical task for human brains, but ML is supremely adept. Through cross-referencing similar patients and comparing their treatment and outcomes, the resulting predictions enable doctors to design a treatment plan that is far more likely to work for their patients.
Personalization in treatment could go a few levels deeper if patients were to utilize wearables and allow said data to feed into a monitoring system that can draw from a richer pool of information to provide more accurate diagnoses. This is especially relevant when patient-centric digital transformation in medical device industry is becoming a top priority.
Top Reasons for AI Adoption in Medical Device Manufacturing
Top Reasons for AI Adoption in Medical Device Manufacturing

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Faster Drug Development
Drug development is perhaps one of those areas in medicine that stands to benefit the most from AI and its allied technologies. The process of developing a drug can be highly resource-intensive, often requiring thousands of person-hours and millions of hours to be sunk into development. A closer examination into the process reveals that the analytical work takes a bulk of the time to go through across the stages of drug development.
An initial step in drug development, identifying pathways, now has so many data sources to draw from, making traditional methods highly ineffective in reaching a definite conclusion in time. For example, some drug companies are looking to leverage AI & ML to search for new indications (uses) for their existing portfolio of products, like finding existing drugs used for other diseases that may be useful in treating COVID-19. This drug repurposing would dramatically reduce the cost and time of bringing a needed therapy to market, improving and saving patients’ lives.
Diagnosing Diseases
In a world that is mired with travel restrictions, reduced hospital visits, and postponement of critical care activities, medical health providers seek alternative methods to engage with their patients in meaningful ways while being conscious of their operating constraints. The tools which enable such interactions will have to be forged by medical device manufacturers, who are increasingly pivoting to a ‘servitization model,’ wherein device manufacturers realize more excellent value from product leasing, allied services, etc.
In this regard, providing Diagnosis-as-a-Service (DaaS) can be particularly appealing to manufacturers and health providers. It eliminates the need for a significant upfront investment and drastically increases the lifetime value from the manufacturers’ standpoint. AI has proven to be highly efficient in diagnosing a whole host of diseases from various data sources, with more advanced capabilities being just around the corner. This is a golden opportunity for device manufacturers to begin investing in AI-enabled ‘smart’ devices that can equip doctors with more powerful tools, thereby transforming healthcare for much of the population.
Real-Life Examples of Medical Device Manufacturers Using AI
Philips Healthcare - IntelliVue Guardian EWS
As wearables continue to demonstrate immense growth potential, Medical device manufacturers are actively looking for ways to integrate them into existing healthcare systems to enable better patient outcomes. A great example is Philips Healthcare’s patient monitoring systems that leverage software, mobile connectivity of vital sign capturing devices for patients waiting in the emergency room, and clinical decision support algorithms to identify suitable patients for early and effective interventions. Dubbed the IntelliVue Guardian Solution, the solution hinges on the usage of Artificial Intelligence to predict the probability of occurrence of a life-threatening crisis amongst patients.
An independent study of this technology within the confines of a waiting room in an emergency department revealed that nearly 80% of all the participants were able to be discharged from the hospital after they visited the waiting room without having to be admitted, based on the capture of critical vitals in the waiting room. Close to 70% of clinicians admitted to the device’s efficacy in helping them identify patients who needed immediate medical assistance.
GE Healthcare - Improving Imaging
GE Healthcare and NVIDIA have been jointly working on integrating the AI platform of the latter into the imaging devices of the former to improve the speed and accuracy of computerized topography(CT) scans. GE healthcare has close to half a million devices in the market, and this decade-long partnership is sure to set the standard for the industry as a whole. CT scans are excellent candidates for AI enhancement. They generate massive amounts of data from the cross-sectional images of various body parts that they capture and lead to better diagnoses.
The appeal of this undertaking lies in the fact that this technology utilizes algorithms to capture more subtle details of the scans, thereby supporting faster diagnoses and reduced errors. Increased speeds translate to reduced radiation exposure, which means more rapid treatment times and improved clinical outcomes. The new CT system is professed to be twice as fast as its predecessor in identifying kidney and liver lesions thanks to the high volume of data available through NVIDIA’s AI platform.
Differentiation Through Digital
If medical device manufacturers are experiencing low growth rates, it’s because of the commoditization of the kind of devices they have been producing. The way forward for such companies is to develop product differentiation through added capabilities and services. AI lends itself perfectly to this goal, just as much as other technologies, such as Augmented Reality (AR), do.
Integrate AI Into Company’s DNA
Medical device manufacturers will have to shed their conceptions of what it means to be a manufacturing company and slowly evolve into ‘MedTech’ companies that increasingly ship ‘smart’ products and related services that feature innovations in hardware, software, and patient and healthcare provider engagement.
Strategic Collaborations
The example of the collaboration between GE Healthcare and NVIDIA is how companies from distinctly different domains can piggyback off each other’s domain expertise to produce genuinely revolutionary outcomes in the healthcare industry. For young manufacturers struggling to gain a foothold in the saturated devices market, collaborating with new-age technology companies would give them access to sophisticated AI technologies that can spawn entirely different product lines.
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