Every great product today, whether a car, an aircraft, or a medical device, runs on more code than ever before. In medtech, this shift from hardware-led innovation to software-defined systems has unlocked new possibilities for intelligence, automation, and precision. But it has also introduced a new challenge: How to sustain increasingly complex software ecosystems over years of regulatory, technological, and clinical change?
To imagine where the industry is heading, we can learn from five sectors that have already faced this transformation. Each offers a valuable lens into how software lifecycle management (LCM) must evolve to meet the next decade of healthcare innovation.
A decade ago, cars were defined by their engines. Today, they are defined by the code that drives them. Advanced driver assistance systems and real-time connectivity have turned vehicles into rolling computers that continuously evolve through software updates.
The same transformation is reshaping medtech. The value of a radiology or therapy system no longer lies in its magnet, gantry, or detector, but in the intelligence of its algorithms. Software now determines image clarity, safety, and workflow efficiency.
Lesson:
Software is not an add-on; it is the product. Lifecycle management must move from maintaining static systems to enabling continuous software innovation, balancing safety and agility.
In communications and networking, service providers operate under a flood of evolving standards for security, data privacy, and performance. Regulatory change is constant, and non-compliance can delay rollouts or compromise trust. Rather than reacting, leading organizations-built compliance into design. They use AI to interpret new regulations, simulate their impact, and generate compliance documentation automatically.
For medtech, this is the next frontier. Frameworks such as MDR, FDA, and cybersecurity equirements must be integrated directly into the software lifecycle, not treated as checkpoints at the end.
Lesson:
Compliance is becoming a design capability. Embedding regulatory foresight early in development ensures that every release is audit-ready before it ships.
Energy infrastructure must operate reliably for decades. Predictive analytics now monitor vibration, temperature, and efficiency to prevent failures and optimize output.
Medtech faces the same long-tail challenge. Sustaining engineering often consumes the majority of lifecycle cost, diverting resources from innovation. Predictive and generative AI can analyze service logs, performance data, and telemetry to detect early anomalies and recommend targeted updates before issues reach the field.
Lesson:
Sustainment is no longer reactive, it is predictive. Lifecycle management must merge maintenance with continuous improvement, using data to extend product life while freeing resources for new development.
In aerospace, reliability and certification requirements mean systems must perform safely for decades. Digital twins and deep-learning models are used to predict material fatigue and optimize modular designs that simplify maintenance and inspection.
Medtech can apply the same philosophy. Complex imaging and therapy systems often require full subsystem replacements when only a single component fails. A modular, software-defined architecture allows selective upgrades and faster servicing while ensuring compliance.
Lesson:
Design for replacement, not redesign. Modularity reduces cost, simplifies certification, and extends system life without interrupting clinical operations.
Consumer technology has mastered the rhythm of constant renewal. Every year brings new product versions, while older devices continue to receive updates and security patches.AI automates testing, regression analysis, and documentation to sustain this dual-speed innovation model.
For medtech, the same dual-track approach - one focused on sustaining products, the other on creating new features, ensures reliability while accelerating time-to-market. Automation and generative AI keep both tracks aligned, compliant, and continuously improving.
Lesson:
Innovation and sustainment are partners, not opposites. The future of medtech software depends on managing both with equal rigor and speed.
Across industries, one truth is clear: software has become the defining layer of innovation, and lifecycle management is the foundation of trust. In medtech, managing that lifecycle is no longer about keeping systems running; it is about keeping innovation alive.
AI-enabled lifecycle management can make every update safer, every release faster, and every product smarter, transforming how healthcare technology evolves. The real breakthrough is not just in what we build, but in how we make it learn.
Let’s design platforms that evolve, adapt, and deliver lasting impact!
Learn how Cyient is enabling the next generation of healthcare lifecycles.
About the Author
Srinivas has 25+ years of experience spanning Consumer Electronics, Biomedical Instrumentation and Medical Imaging . He has led research and development teams, focused on end-to-end 3D/4D quantification applications and released several "concept to research to market" solutions. He led a cross functional team to drive applied research , product development , human factors team, clinical research, external collaboration and innovation.