There’s an increasing need for speed in medical device research and development. The market is growing at a compound annual growth rate of 5.2 percent, projected to reach $671 billion by 2027. 1 Rising competition is making time to market more critical than ever, with the sector facing an innovations race to secure intellectual property, build early customer loyalty, set industry standards, and deliver new breakthroughs to patients.
Factors including an aging global population and associated increase of chronic diseases and investor expectations that are shifting toward shorter development cycles as well as faster return on investment and demand from healthcare systems are exacerbating the situation. Simultaneously, rising device complexity, stricter global regulations, and demand for more clinical data, usability testing, and cybersecurity validation — coupled with clinical trial recruitment challenges — are contributing to slower approval timelines.
Artificial intelligence (AI) is emerging as a tool to balance these challenges and facilitate the goal of bringing new innovations to market faster. And while AI undoubtedly has the potential to play a transformative role in medical device R&D, it must be approached with caution and utilized in a supportive capacity. People remain key to competing in today’s medical device development landscape and it’s vital that teams move as one to make timely decisions and develop confident, powerful strategies that ensure innovations are delivered successfully, and to a receptive market.
Approach with Caution
AI is having an impact across the entire supply chain — from design and development to clinical trials, manufacturing, and postmarket surveillance. AI-driven algorithms can analyze design data to optimize design features, materials, and manufacturing processes, leading to more efficient and effective devices. AI tools can help with regulatory compliance by documenting manufacturing processes, monitoring adherence to regulatory standards, and streamlining documentation for approval processes.
In addition, machine learning models can detect defects and inconsistencies in components or assembled devices through image recognition and sensor data analysis, improving quality assurance. And this only scratches the surface of the possibilities AI can achieve for the medical device manufacturing sector, making it very well positioned to be taken advantage of in the longer term.
However, there are pitfalls to be aware of that relate to ethical risk, bias and regulatory complexity. For instance, training data bias can skew outputs, risking inequitable outcomes. Overreliance on AI can erode human critical thinking and accountability. And AI’s “black box” nature can complicate compliance with transparency requirements; making it difficult to understand how decisions are made, which can be problematic for safety critical applications like medical devices.
In addition to this, AI systems often require large amounts of sensitive patient and manufacturing data to provide usable results, raising risks related to data breaches and privacy violations. AI systems usually require constant monitoring, validation, and updates to ensure consistent performance over a long period of time. Alongside these issues, there is what can be a sizable investment requirement based on the need for the right technology, infrastructure, talent and training to use AI to its fullest potential. The skills gap and workforce impact can be added to this, not only in terms of a shortage of professionals with combined expertise in AI and the medical device industry, but also the risk of job displacement where certain roles could become automated, which can cause resistance from within and therefore have a negative impact on the wider organization.
A Shift in Perspective
To optimize use of AI and mitigate risk, its perception must shift from one of decision maker to collaborative partner. This means recognizing AI’s ability to stimulate creativity and augment human judgement, through provision of data-driven insights, while 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 expediting time-to-market. AI can support the process 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 to bring innovations to market faster 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 come together to make timely decisions and develop confident, powerful strategies that ensure innovations are delivered not just quickly but successfully, and to a receptive market?
Beyond AI
Medical device companies must balance AI’s efficiency with human expertise to meet ever ambitious timelines and drive ethical, patient-centered 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. 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 optimizes teamwork by providing a space for teams to collaborate effortlessly across time zones and functions. More than this, it drives unity — ensuring that people are driven by shared purpose and strategy and coordinating 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.
Cultivating a Successful Coexistence
AI is in the room but will it bring innovations to market quicker? In short, the answer is no. Not by itself at least. AI isn’t a silver bullet to success, but it does have potential to add value if approached as a collaborative, rather than directive tool. The real challenge medical device companies face on their mission to deliver innovations to patients faster is their people, and the way they communicate and operate as part of a team.
To empower disparate, multidisciplinary 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 cocreate 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 AI amplifies human 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.
Reference
- “Medical Devices Market Size to Hit Around US$ 671.49 Bn by 2027,” Precedence Research, Ottawa, ON, Canada.
This editorial was written by Janice MacLennan, Founder and CEO of Nmblr, a strategy and collaboration platform that partners with biopharma teams, based in New York, NY. For more information, visit here .
