Tricog Health

Not always in a technical sense, but transparency about the product is increasingly expected. Many machine learning models cannot give a simple step-by-step reason for each output, so regulators focus on clear information for users: intended use, inputs, how the model was developed and tested, performance, and known limitations.

In the US, one of the criteria for clinical decision support to fall outside device regulation is that a healthcare professional can independently review the basis for its recommendations. FDA has also published transparency principles for machine learning devices with Health Canada and MHRA.

For buyers, a vendor's ability to document how its model works and where it fails is often more useful than heat maps or confidence scores alone.

AI is used to screen large volumes of ECG data from patches, wearables and remote monitors, flagging possible arrhythmias or recordings that need clinician review. This helps manage the large amount of data produced by continuous monitoring.

Important distinctions for buyers:

• A consumer feature on a wearable and a clinical-grade analysis service can have very different intended uses and authorizations.
• Authorization often covers specific recording hardware, leads and populations.
• Signal quality and motion artifacts affect results.

In the US, software that analyzes physiological signals does not meet the criteria for non-device CDS. Confirm each product's status, and check how alerts are routed to clinicians and documented.

Automation bias is the tendency of people to over-rely on a suggestion from an automated system, either following an incorrect output or missing something because the system did not flag it.

FDA's Clinical Decision Support Software guidance (issued January 2026) discusses automation bias and notes it increases in urgent situations where there is not enough time to consider other information. This is one reason FDA does not consider software intended for critical, time-sensitive decisions to meet the criteria for non-device CDS.

Organizations reduce automation bias through user training, clear display of the basis and limits of an output, workflows that keep independent clinical review, and monitoring of how the tool is actually used.