Articles that explain how diagnostic studies are designed and reported, including bias, blinding, missing data, thresholds, indeterminate results, predictive values, and types of evidence. This page lists all 9 Connected Diagnostics Evidence guides on the topic, each with its opening lines so you can pick the one that matches your question.
Connected Diagnostics Evidence is an independent publication. These guides are for education and do not replace care from a qualified professional.
Guides in this section
Verification Bias in Diagnostic Research: When Not Everyone Gets the Same Comparison
By Connected Diagnostics Evidence Editorial Team | Updated September 15, 2026 You’re reading about a connected diagnostic device — maybe a digital otoscope, a phone-linked skin scanner, or a home test that pairs with an app — and the maker cites a study claiming the device is highly accurate.
Indeterminate Diagnostic Results: Why Unreadable Images Belong in the Study Counts
When a connected diagnostic device study reports how well it works, some images or readings almost always come back unclear, blurry, or impossible to interpret. These are called indeterminate results . A trustworthy study does not quietly drop them from the count.
Diagnostic Cutoffs in Connected Devices: Why a Threshold Changes the Result
When a connected diagnostic device — a phone-linked otoscope, a home ECG patch, a digital rapid-test reader — tells you "positive" or "negative," that word is standing in for a number crossing a line. The line is called a cutoff, or threshold. Move the line, and the same raw measurement can flip from negative to positive.
Bench, Simulation, and Clinical Evidence: What Each Proves
Bench testing, simulated-use testing, and clinical evidence each answer a different question about a connected diagnostic device. Bench testing checks whether the hardware or software meets a technical spec in the lab. Simulated-use testing checks whether a realistic user can operate it correctly.
Blinding in Diagnostic Device Studies: Why Independent Review Can Change What Results Mean
Blinding (also called masking) means keeping certain people in a study from knowing which result a device gave, or which test a patient actually received.
How to Read a Diagnostic Device Study for Missing Data and Withdrawn Participants
Missing data means information a diagnostic device study was supposed to collect but didn't. Withdrawn or excluded participants are people who were enrolled but never made it into the final results. When either happens without explanation, a study's reported accuracy may not reflect everyone who started it.
Bias and Representation in Connected Diagnostic Evidence: Questions to Ask of a Study
A diagnostic-device study can be well-designed and still not tell you what you think it tells you. That happens when the people, settings, or data in the study don't match the situation the device is actually being used for.
Positive Predictive Value: How Prevalence Changes Results
Positive predictive value (PPV) is the probability that a positive result — including one from a connected or app-based screening tool — reflects a true case of the condition rather than a false alarm. PPV shifts with how common the condition is in the population tested, something sensitivity and specificity alone don't reveal.
How to Read a Diagnostic-Accuracy Study of a Connected Medical Device
A diagnostic-accuracy study measures how often a health app or connected device gets it right — and how often it doesn't. Before trusting a diagnostic claim from a smartphone sensor, camera-based screening tool, or other connected medical device, check four things: the reference standard used, who…