The drive to bring innovative new medical devices to market quickly, compliantly and cost-effectively is prompting record investment in tech. Artificial Intelligence (AI) and machine learning is being embraced to improve R&D efficiency across device development, with the global AI in medical devices market projected to grow at a compound annual growth rate of 26.2%, expanding from $19.1 billion in 2024 to $143.5 billion by 2037[i].

Similarly, cloud computing is being leveraged for data storage and analysis. For example, digital twin technology is being used to optimise development processes. And while the adoption rate of automation falls behind other sectors, such as automotive, due to regulatory and validation-shaped obstacles, there are notable instances of automation and robotics gaining traction in medical device development. One example of this can be found with Tegra Medical – a contract manufacturer of medical devices that successfully doubled production throughput and reduced operator staffing positions by deploying universal robots (collaborative arms) to repeat high-volume tasks without changing its validated processes[ii].

However, while these tools can undoubtedly add value (90% of medical device organisations view digital transformation as essential to future growth[iii]), is the sector’s drive for greater efficiency inadvertently introducing new workforce-related risk? Are medical device companies sleepwalking into a skills crisis?

Innovation under the microscope

Medical device development is transforming before our eyes, with innovations spanning AI and machine learning, 3D printing and robotics, the Internet of Medical Things, digital twin technology, cloud computing and big data analytics promising reduced development cycles and lower R&D costs.

Where AI is concerned, many medical device firms are starting to realise faster and smarter design, with algorithms able to analyse patient data and simulate device performance before physical prototypes can even be made. AI is also being used to inform predictive maintenance and real-time monitoring to catch potential device failures before they occur.

Looking to other technologies reshaping the medical device sector; 3D printing is playing an increasingly prevalent role in speeding up prototyping activity, with models now created in hours instead of weeks. Likewise, digital twin technology is enabling medical device developers to build virtual replicas of patients or devices and simulate real-world usage and outcomes. Philips is pioneering this technology, having developed cardiac digital twins to test its cardiovascular devices in silico. Finally, the increasing reliance on the Internet of Medical Things is supporting the development of medical devices in numerous ways, spanning design, testing, commercialisation and post-market performance.

These examples are not exhaustive, but they do paint a picture of the sector’s race to adopt new tools and technologies to drive better performance. Whether that’s measured by an expedited time to market (through AI-accelerated research and prototyping), improved personalisation (through tailoring devices to individual physiology and conditions using real-time data), enhancing safety and compliance (through predictive analytics to ensure regulatory standards are met earlier in the design phase), or R&D cost savings (through use of automated testing and reduced clinical trial durations). And while these achievements are of course to be celebrated, after-all they’re each playing a role in advancing health outcomes for countless patients across the globe, they also need to be approached with a degree of caution.

Weighing up the real risk to people

In the race to develop winning strategies that propel businesses and products forward, we mustn’t lose sight of our most valuable assets: our people. People cannot be collateral damage of digital transformation, so while adoption of new technology offers clear benefits, it’s important to remember that it can also introduce critical challenges to overcome. One such challenge is the potential de-skilling of people.

Let’s look at AI again. We know that AI systems can perform tasks that traditionally require in-depth expertise, such as device design simulations. But, as AI becomes more deeply embedded in device development, we need to look at the byproduct of this. We need to ask: what’s the impact on people? Are we becoming over-reliant on algorithms? And is this likely to lead to an erosion of the skills we need? Radiology has been highlighted as an area particularly at risk in this respect. A 2024 article in The Lancet Digital Health Journal cautioned that if radiologists begin to rely excessively on AI for image interpretation, they may lose the ability to independently evaluate complex or edge-case scenarios, where AI may fail[iv]. While this example relates to diagnostics, rather than device development, the risks remain.

Loss of ‘hands-on’ knowledge and skills honed by practical exposure, trial and error and nuanced judgement are also at risk. After all, AI systems don’t pass on this type of learning, they replace it. This means, as engineers shift to managing AI models instead of engaging in iterative design, manual skills, material familiarity and creative problem-solving may start to decline. Indeed, some experts have gone as far as to suggest that AI systems in health tech risk ‘hollowing out’ technical roles, as people shift from creative input to passive oversight.[v] We also must give thought to the impact this shift may have on learning and development opportunities for future talent entering medical device companies. The black box nature of AI systems also represents a risk to people; when decision making processes are not easily explainable (even to experts) one outcome can be an over-trust in the outputs and a lack of critical thinking. This can lead to people defaulting to AI even when its outputs are flawed. This theory is substantiated by a 2023 study that showed that even when clinicians were given incorrect AI model outputs with explanations, many still followed the AI, sometimes worsening diagnostic accuracy.[vi]

A shift in perspective

To optimize AI use and mitigate the risks to people, our perception of it must shift from one of decision maker to collaborative partner. This means recognising its ability to stimulate creativity and augment human judgement, through provision of data-driven insights for example, whilst acknowledging that it lacks contextual and ethical reasoning.

When we appreciate that AI requires human oversight to deliver value, we in turn acknowledge that it is people who can make the biggest difference in influencing outcomes. AI can support but final decisions must involve clinicians, engineers and commercial professionals to ensure safety, and clinical and commercial relevance. Battelle’s AI-driven NeuroLife technology, designed to restore motor function in tetraplegic patients, provides a prime example of this. Looking to extend use to additional indications, namely stroke rehabilitation, the company faced new challenges that AI alone couldn’t overcome. Clinician input was crucial to adapting the technology to benefit the highly variable motor impairments of stroke rehabilitation patients, developing therapy protocols that aligned with individual rehab goals and interpreting data in a therapeutic (not just technological) context.

Harnessing the supportive value of AI, preserving a skilled workforce and deriving the greatest value from people is dependent then on teams of people and their ability to move as one. And herein lies the next challenge: how can people working across functions, geographies and time zones at medical device organisations come together to make timely decisions and develop confident, powerful strategies that ensure innovations are delivered successfully, and to a receptive market?

Optimising teams, preserving skills

Medical device companies must balance technology’s efficiency with human expertise to meet ever ambitious timelines and drive ethical, patient-centred innovations to market. For instance, AI alone cannot solve the problem of slow decision making, miscommunication, siloed working or weak strategy that leads to missed opportunity and wasted investment. Harnessing bright ideas, bringing together people’s experiences no matter their job function, making quick and effective decisions – all these critical steps should not be forgotten when delivering innovations to market. To enable this, company leaders must start with their people by providing pathways that enable participation from the best thinkers to offer diverse insights that drive smarter decisions.

Successful outcomes should be based on every function, whether local, global or virtual, being able to work in unison to collectively make high stakes decisions at the right moment. Technology platforms that support teams to move as one should be a key consideration for medical device companies. Bringing people together through collaborative strategy platforms serves to build momentum, helping medical device companies to sustain and accelerate pace by eliminating friction. It reinforces alignment, keeping projects on track and increasing accountability by aligning insights, goals and actions. And of course, it optimises teamwork by providing a space for highly skilled teams to collaborate effortlessly across time zones and functions. More than this, it drives unity – ensuring people are driven by shared purpose and strategy and co-ordinating action by aligning every function to one strategy. Finally, it empowers decision-making to allow medical device companies to make more confident, informed and strategic choices.

Waking up to the potential of teams moving as one

To avoid sleepwalking into a skills crisis, medical device organisations must balance investment in digital tools with continued focus on nurturing their biggest asset: their people. People must remain at the heart of decision making and purpose-driven, albeit typically multi-disciplinary and geographically dispersed teams, must be empowered to share and collaborate to co-create the strategies required to enhance patient access, experience and outcomes.

To empower disparate, multi-disciplinary teams to move as one, it’s necessary to remove the geographical and departmental barriers. By providing platforms like Nmblr, people can collaborate to discover insights that unlock opportunities; build a shared vision to explore these opportunities, overcome obstacles and co-create paths to success; define a confident strategic direction; and align it across key teams and markets to drive efficiency, agility and lasting success.

Ultimately, the future of medical device development lies in augmented intelligence – where technology like AI amplifies human skills and potential without replacing it and where teams are supported to bring innovations to market in the right shape, at the right time and delivered to a receptive market.

Editor’s Note: Janice MacLennan is CEO of Nmblr, the Biopharma and MedTech collaborative strategy platform. With over 30 years’ experience helping Biopharma and Medical Device companies tackle complex commercial challenges and develop winning strategies, she brings deep industry insight and personal passion to her work. Janice has dedicated her career to supporting the delivery of breakthrough therapies through consultancy partnerships with big and mid-sized companies across the globe. At Nmblr, she leads efforts to humanize strategy – empowering teams to align, adapt, and move as one to make better decisions and ensure innovations are delivered successfully, and to a receptive market.

[i] https://www.researchnester.com/reports/ai-in-medical-devices-market/2944

[ii] https://www.roboticstomorrow.com/article/2015/11/how-universal-robots-doubled-production-at-tegra-medical/7256

[iii] https://zipdo.co/digital-transformation-in-the-medical-device-industry-statistics/

[iv] https://www.thelancet.com/journals/landig/article/PIIS2589-7500%2824%2900095-5/fulltext

[v] https://www.workplaceinsights.co.uk/ai-knowledge-work-transformation

[vi] https://www.healthcaredive.com/news/healthcare-ai-bias-clinical-accuracy-jama-study/703151/