Portal Telemedicina

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.

General diagnostic imaging software stores, displays and manipulates images, for example PACS viewers, 3D reconstruction or measurement tools operated by the user. Radiology AI adds automated analysis that produces its own output, such as a detection, triage flag or quantitative result.

The distinction matters for evaluation:

• Viewing and processing tools are judged mainly on image quality, usability and integration.
• AI analysis tools also need evidence on accuracy, populations studied, failure modes and monitoring.

Both can be regulated medical devices depending on intended use; in the US many image management and processing functions fall under device classifications. Many vendors now combine the two, so ask which functions are AI-based and which are covered by a regulatory authorization.