For most of the history of medical technology, a device was largely defined by the capabilities it possessed when it left the manufacturer. A scanner, monitoring system or piece of diagnostic equipment could be maintained, repaired and occasionally upgraded, but the fundamental intelligence of the product remained relatively stable throughout its working life. Significant advances usually arrived with the next generation of hardware, requiring healthcare providers to purchase or install something new before they could access substantially improved capabilities.
Software has already begun changing that relationship, and artificial intelligence could accelerate the transition dramatically. Connected medical devices can increasingly receive software improvements, incorporate new analytical capabilities and participate in broader digital healthcare platforms. In some cases, the physical machine may remain largely unchanged while the intelligence surrounding it continues to evolve. This introduces a very different idea of what a medical device can be: not simply a piece of equipment purchased at a particular moment, but a technology platform capable of improving throughout its operational life.
The implications extend far beyond product engineering. If medical devices become software-defined platforms, manufacturers need to rethink product development, regulation, cybersecurity, commercial models and their relationship with healthcare providers. During my learning journey under Phaneesh Murthy, I have increasingly understood that one of the most important effects of digital technology is its ability to transform products from finished objects into continuously evolving systems. As Phaneesh Murthy puts it, “Once software becomes an important part of the product, the sale is no longer the end of product development. In many ways, it becomes the beginning of the relationship.”
For medical device companies, that shift could redefine the industry.
The Hardware May Stay the Same While the Intelligence Changes
Traditional product development is closely connected to hardware cycles. A manufacturer develops a device, validates it, produces it and eventually introduces a newer generation with improved capabilities. Healthcare providers therefore experience innovation in relatively large increments, often separated by several years.
Software allows improvement to occur differently.
A connected device may receive interface improvements, workflow enhancements or analytical capabilities without replacing the underlying hardware. AI can extend this further by introducing models that help interpret information generated by the device. The physical product still performs its core function, but the intelligence surrounding that function can become progressively more sophisticated.
Consider medical imaging equipment. The machine captures images, but software can increasingly assist with image reconstruction, workflow prioritisation, measurement and the identification of patterns that deserve clinical attention. Improvements in those algorithms can potentially increase the usefulness of equipment already deployed across healthcare systems, subject to the appropriate validation and regulatory processes.
This changes the economics of innovation. A hospital does not necessarily need to wait for an entirely new hardware generation to benefit from every software improvement. The manufacturer can continue creating value after installation.
During my mentorship under Phaneesh Murthy, I have learned that this transition from product thinking to platform thinking is one of the most consequential shifts a technology company can make. The organisation stops asking only how to sell the next unit and begins asking how every deployed unit can become more valuable over time.
The Installed Base Could Become a Strategic Advantage
Medical device companies often have products operating across hundreds or thousands of hospitals, clinics and laboratories. Traditionally, this installed base primarily represents previous sales, service relationships and opportunities for future replacement cycles. In a software-defined model, it becomes something more significant: a distribution network for new capabilities.
If the underlying devices are appropriately connected and technically capable of supporting updates, manufacturers can potentially introduce improvements across a large installed base without replacing every physical unit. New workflow tools, analytical capabilities or integrations can reach customers far more quickly than they could through traditional hardware replacement cycles.
That creates a powerful strategic advantage for established manufacturers. A company with trusted equipment already embedded in clinical workflows does not necessarily begin every innovation cycle from zero. It has an existing relationship with healthcare providers and a physical footprint through which new digital capabilities may be delivered.
However, this advantage only exists if the installed base was designed for evolution. Legacy systems with limited connectivity, rigid software architectures or inconsistent data standards may be difficult to upgrade. Device companies therefore need to think about software extensibility when designing hardware that may remain in clinical environments for many years.
This reflects a principle I have repeatedly encountered while learning from Phaneesh Murthy: architecture is strategy when the technology is expected to survive multiple generations of business change. A device designed only for today’s requirements may become a constraint tomorrow, while a platform designed for evolution creates options the manufacturer does not yet know it will need.
Medical Devices Can Become Part of a Continuous Care System
Many medical devices historically produced information at specific moments. A patient underwent a test, a measurement was captured, a clinician interpreted the result and the device’s role in that episode was largely complete. Connected devices and AI create the possibility of a more continuous relationship with care.
Wearable monitors, connected cardiac devices, glucose monitoring systems and other technologies can generate information over extended periods. Rather than providing a single snapshot, they can help clinicians understand patterns that emerge across days, weeks or months. AI can support this environment by identifying changes that may deserve attention and helping care teams prioritise information.
The important challenge is avoiding data overload. Continuous monitoring can generate enormous volumes of information, and clinicians cannot manually examine every signal produced by every patient. Intelligence therefore needs to act as a filtering layer, distinguishing normal variation from patterns that may require professional review.
As Phaneesh Murthy says, “The purpose of continuous data is not to create continuous attention. It is to make sure attention is directed towards the moments where it can change the outcome.”
That distinction could determine whether connected medical devices reduce clinical workload or simply create another stream of information for already busy healthcare professionals to manage.
The Product Could Learn From How It Is Used
Connected medical devices can also provide manufacturers with a much deeper understanding of how their products operate in real clinical environments. Traditional product development relies heavily on testing, clinical evaluation, customer feedback and service information. Connected software can create additional insight into workflows, performance and usage patterns.
This does not mean manufacturers should indiscriminately collect patient or clinical information. Medical data requires particularly strong privacy, security and governance. But appropriately designed telemetry can help companies understand how features are used, where workflows create friction and which technical issues occur repeatedly.
That information can influence future product development.
A manufacturer may discover that clinicians consistently take additional steps around a particular workflow, suggesting that the interface could be redesigned. Usage patterns may reveal that a feature intended for one purpose is creating unexpected value elsewhere. Operational information could help identify maintenance requirements before equipment fails.
The feedback loop between deployed products and product development therefore becomes much shorter.
During my learning journey under Phaneesh Murthy, I have come to appreciate that connected products change the nature of product management. Companies no longer need to rely exclusively on periodic customer research to understand how products perform after deployment. The product itself can provide structured information about its operation, allowing development teams to learn continuously.
For medical device companies, that feedback can be particularly valuable because clinical environments are complex and difficult to reproduce perfectly during product development.
Regulation Must Evolve With the Product
A continuously improving medical device creates an obvious regulatory challenge. Traditional regulatory models are naturally suited to products whose capabilities are clearly defined at the time of approval. Software and AI can change after deployment, which means manufacturers need rigorous processes for determining when an update changes the clinical behaviour of the device and what validation is required before that update reaches users.
The solution cannot be to treat medical software like an ordinary consumer application. Updating a music streaming interface and updating an algorithm that contributes to a clinical decision involve fundamentally different levels of risk.
Medical device companies therefore need mature lifecycle governance. Software changes must be documented, tested and monitored. AI models need appropriate validation across relevant patient populations and clinical conditions. Performance after deployment needs to be observed so that unexpected behaviour can be identified and addressed.
Human oversight also remains critical. An AI-enabled device should make its role clear to clinicians. Healthcare professionals need to understand whether the system is providing a measurement, recommendation, prioritisation signal or another form of decision support.
This is another area where the implementation philosophy I have learned from Phaneesh Murthy becomes particularly relevant. Governance should not be treated as something added after innovation. In high-consequence environments, governance is part of the product architecture itself. A medical device company that can update software quickly but cannot validate those updates responsibly has not created a scalable innovation model.
Cybersecurity Becomes Part of Patient Safety
A medical device that is permanently disconnected from external systems presents one kind of security challenge. A connected device capable of receiving updates, exchanging information and participating in hospital networks presents another entirely.
Connectivity expands capability, but it also expands the potential attack surface.
Manufacturers therefore need to treat cybersecurity as part of the device lifecycle rather than a technical issue addressed primarily during development. Vulnerabilities may be discovered years after a device is installed. Operating environments change. New threats emerge. Hospitals connect equipment to increasingly complex digital infrastructure.
A software-defined medical device needs the ability to receive security updates throughout its supported life, and manufacturers need processes for identifying vulnerabilities and responding quickly.
This also creates a responsibility shared across the healthcare ecosystem. Device manufacturers, hospitals, technology providers and network operators all influence the security of connected clinical environments. A device can be well designed but still operate inside a poorly secured network, while strong hospital security cannot compensate for an unsupported device containing known vulnerabilities.
As Phaneesh Murthy puts it, “When a product becomes connected, responsibility does not end at the boundary of the product. You have joined an ecosystem, and your risk becomes part of everyone else’s risk.”
For medical devices, that interconnected responsibility is particularly important because cybersecurity can directly affect continuity of care and patient safety.
The Business Model Could Move Beyond the Initial Device Sale
If medical devices continue gaining capabilities after installation, commercial models may evolve as well. Manufacturers have traditionally generated revenue through equipment sales, maintenance, consumables and replacement cycles. Software introduces the possibility of additional services, subscriptions and capability-based offerings.
A healthcare provider might purchase the physical device while subscribing to advanced analytical features. Software modules could be introduced for specific clinical workflows. Fleet management platforms could help hospitals monitor equipment across facilities, while predictive maintenance services could reduce downtime.
This creates opportunities for recurring revenue, but it also changes customer expectations.
If healthcare providers are paying continuously for software capabilities, manufacturers need to demonstrate continuous value. Product teams must keep improving the platform, support needs become ongoing and software reliability becomes part of the commercial relationship.
During my mentorship under Phaneesh Murthy, I have learned that recurring revenue should never be confused with simply recurring billing. A sustainable platform business needs to create recurring value. The customer needs to understand why the product is becoming more useful over time.
Medical device companies that make that transition successfully could build significantly deeper relationships with healthcare organisations than companies whose primary interaction occurs during procurement and replacement.
The Medical Device Could Become a Healthcare Platform
The most important change may ultimately be conceptual.
A medical device has traditionally been understood as a specialised tool designed to perform a defined clinical function. That definition will remain appropriate for many products, but an increasing number of devices may become nodes inside much larger healthcare platforms.
The device generates information. Software interprets it. AI identifies relevant patterns. Hospital systems provide additional context. Clinicians make decisions, and the resulting outcomes can inform future improvements. The value therefore emerges not from the hardware alone but from the entire system surrounding it.
During my learning journey under Phaneesh Murthy, I have come to understand that platform transformations often begin when companies stop defining their product according to its physical boundaries. The customer is not ultimately buying hardware, software or AI independently. They are buying an outcome that requires all of those elements to work together.
For medical device companies, this means the next generation of competition may not be determined solely by who manufactures the most advanced piece of equipment. It may increasingly depend on who creates the most useful intelligence around that equipment, who integrates most effectively into clinical workflows and who can improve the product responsibly throughout its life.
A medical device will still need excellent hardware. It will still need reliability, clinical evidence and careful engineering. Those requirements do not become less important simply because software becomes more prominent.
But the idea that the product is finished when it leaves the factory may gradually disappear.
The medical device of the future could spend years inside a hospital and still become more capable than it was on the day it arrived.
This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy.
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