What Software Teams Get Wrong Most Often
Medical device software rarely fails because teams lack technical capability. More often, it fails because engineering, clinical intent, and regulatory expectations drift out of alignment as development accelerates.
We see this pattern repeatedly across SaMD, SiMD, and AI/ML-enabled devices. Teams move quickly to build functionality, but foundational decisions around intended use, risk, quality system integration, and change management are left unresolved until late in development — or worse, until an FDA submission or inspection forces the issue.
Below are the most common pitfalls software teams encounter when developing regulated medical device software, and why they create downstream risk.
Scope creep is not unique to medical device software, but its consequences are.
Features are added to address edge cases, customer requests, or competitive pressure without formally reassessing:
What starts as a “small enhancement” can quietly expand the clinical claims or decision-making role of the software. When this happens without corresponding updates to documentation, risk analysis, and design controls, teams create gaps that are difficult to explain to regulators later.
In regulated software development, scope discipline is not about limiting innovation — it is about maintaining traceability between what the software does and what the company claims it does.
Many software teams begin development with a general idea of the problem they are solving, but not a precise, regulator-ready definition of intended use and indications for use.
This lack of clarity creates cascading issues:
For AI/ML-enabled software in particular, vague intended use statements make it difficult to justify model outputs, training data selection, and performance metrics. FDA expectations increasingly assume that software behavior, clinical role, and user population are clearly defined early — not retrofitted later.
Software teams often design for technical performance first and clinical workflow second. The result is software that technically works, but does not fit cleanly into real-world use environments.
Common red flags include:
From a regulatory perspective, unclear workflow integration increases the risk of misuse and misinterpretation. From a quality perspective, it undermines the effectiveness of risk controls that rely on user behavior.
Clinical workflow is not just a usability concern — it is a safety and compliance concern.
Another frequent issue is uncertainty around the regulatory pathway well into development.
Teams may delay decisions about:
When regulatory strategy lags behind engineering, documentation becomes reactive instead of intentional. This is especially problematic for software that evolves rapidly or incorporates machine learning, where FDA expects a clear total product lifecycle perspective.
Early regulatory alignment does not slow development — it prevents rework, surprises, and credibility gaps later.
In many organizations, quality and regulatory functions are consulted after major architectural decisions have already been made.
This creates tension between:
When QMS processes are treated as an afterthought, teams struggle with design controls, risk management, and change control under pressure. The result is often retroactive documentation that lacks consistency and audit resilience.
Effective software teams integrate quality and regulatory input as part of development — not as a final checkpoint.
Most teams acknowledge the need for a risk-based approach. Fewer implement it rigorously.
Common gaps include:
A true risk-based approach requires continuous reassessment as features evolve, data changes, and post-market feedback emerges. Treating risk management as a static exercise undermines both safety and compliance.
Even within software organizations, teams are often siloed. Engineering, data science, and quality may operate on different timelines with limited coordination.
This lack of synchronization shows up as:
For AI/ML devices especially, FDA expectations increasingly emphasize controlled change management across the total product lifecycle. When teams are not aligned, maintaining compliance becomes increasingly difficult as the product scales.
Post-market surveillance is often treated as a reporting obligation rather than a feedback mechanism.
For software and AI/ML devices, this leads to:
Without structured change management, teams risk making updates that unintentionally affect safety, performance, or regulatory status. FDA scrutiny in this area continues to increase, particularly for adaptive or learning systems.
The most successful medical device software teams treat regulatory and quality requirements as design inputs, not constraints.
That means:
Medical device software development is not just about building the right functionality. It is about building it in a way that is defensible, traceable, and sustainable under regulatory scrutiny.
If you want help pressure-testing your software development approach, especially for SaMD or AI/ML-enabled devices — this is exactly where early quality and regulatory alignment pays dividends.