Krutarth Trivedi on The Hidden Infrastructure Behind Modern Medicine

Authored by James Carnell

When people think about medical innovation, they often picture surgeons using robotic systems or AI software interpreting scans. Far less visible is the engineering infrastructure that makes these technologies function safely inside hospitals, operating rooms, and critical care environments. As healthcare becomes increasingly software-driven, the reliability of these unseen systems is emerging as a key challenge in modern medicine.

According to MedTech software engineer and robotics researcher Krutarth Trivedi, software now plays a central role in nearly every stage of healthcare delivery. However, he stresses that intelligence alone is not enough.

“Healthcare systems are moving toward a future where software influences nearly every stage of care,” Trivedi says. “But in medicine, intelligence alone is not enough. The systems also have to be dependable under pressure, because clinicians rely on them in moments where precision directly affects patient outcomes.”

This reliability challenge is growing as hospitals adopt robotics-assisted therapies, AI-enabled imaging systems, and automated clinical workflows. Industry forecasts highlight the scale of this shift. Fortune Business Insights projects the global medical robotics market will exceed US$46 billion by 2032, driven by minimally invasive procedures and automation. McKinsey & Company estimates that AI could generate US$200 billion to US$360 billion annually in healthcare value through operational efficiency, decision support, and diagnostics.

Despite rapid innovation, Trivedi believes the central challenge is no longer building more advanced systems, but ensuring they operate safely and consistently in real clinical environments.

“Many assume it’s still about building more advanced systems,” he explains. “But the real challenge is whether those systems can reliably operate inside high-risk medical environments over time.”

This is especially critical in robotic surgery, organ preservation, radiation therapy, and cardiovascular intervention, where software performance directly impacts patient safety. Throughout his MedTech career, Trivedi has worked on robotic teleoperation platforms for surgical workflows, organ care systems for transplantation, imaging algorithms for cardiovascular planning, and robotic positioning systems for radiation therapy.

His engineering philosophy prioritizes reliability alongside capability.

“In robotics and AI, it’s easy to focus only on capability,” he says. “But in healthcare, reliability engineering becomes equally important. The software has to behave consistently even when the clinical environment becomes unpredictable.”

Rather than treating medical technologies as standalone products, he emphasizes long-term stability, explainability, and integration into clinical workflows under real-world conditions.

Trust and regulatory accountability are also becoming increasingly important as software takes on a larger role in healthcare. One of the biggest shifts in MedTech, he notes, is the growing focus on compliance and transparency.

“As AI-assisted systems become more embedded in clinical workflows, regulators have increased scrutiny around software traceability, risk management, cybersecurity, and lifecycle validation,” he says.

Standards such as IEC 62304 and ISO 14971 now play a central role in medical software development, guiding risk management, verification, and clinical safety documentation.

“My own work in FDA-compliant medical device development has required strict adherence to these frameworks, including traceable testing and validation aligned with regulatory submissions,” Trivedi notes.

He believes the success of future healthcare systems will depend not only on technical sophistication, but also on transparency and trust.

“I believe the future of intelligent healthcare systems depends on whether engineers can make complex technologies both understandable and operationally transparent,” he says. “Clinicians do not need systems that simply appear intelligent. They need systems whose behavior can be trusted consistently in real patient-care settings.”

His perspective aligns with a 2023 World Health Organization report, which identified trust, transparency, and governance as key barriers to global AI adoption in healthcare.

Trivedi’s path into MedTech began outside healthcare. Before entering the field, he worked in embedded systems and industrial automation, developing low-latency software for IoT devices and real-time hardware systems.

He later pursued robotics research at Worcester Polytechnic Institute, focusing on robotic manipulation, active vision systems, and human-robot collaboration. One project involved a robotic grasping system that achieved 96.5% accuracy in identifying and manipulating previously unseen objects using machine vision.

“Those experiences shaped how I understand complex physical environments,” he explains, “a perspective that became essential in healthcare engineering.”

For Trivedi, healthcare presents a uniquely demanding engineering environment due to its dynamic, time-sensitive, and human-centered nature.

“Medical systems operate in environments that are dynamic, time-sensitive, and deeply human,” he says. “The challenge is not only designing advanced software, but ensuring clinicians can realistically integrate these systems into demanding workflows.”

As healthcare continues its shift toward robotics-assisted procedures, AI-supported diagnostics, and software-defined infrastructure, engineers who can balance innovation with safety, reliability, and trust will play an increasingly important role.

Ultimately, Trivedi believes the goal of healthcare technology extends beyond intelligence.

“The goal is not simply building more intelligent machines,” he says. “It is ensuring that the technologies shaping the next generation of medicine remain dependable when patients and clinicians need them most.”