Question 1

Is AI-based radiology software FDA approved?

Accepted Answer

Many AI radiology tools on the U.S. market have received FDA clearance, most commonly through the 510(k) pathway for moderate-risk devices, rather than full FDA "approval," which is a separate and generally more rigorous process. Clearance status varies by product and by the specific clinical use the software is cleared for, so it is worth checking a vendor's specific clearance rather than assuming clearance for one use extends to another. See FDA Clearance (510(k)).

Question 2

Can AI replace a radiologist or physician?

Accepted Answer

Current clinical AI tools are designed to assist a clinician, not replace one. Most deployed systems, including radiology triage software and ambient documentation, are built as human-in-the-loop tools: the AI produces a draft finding, flag, or note, and a licensed clinician reviews, edits, or confirms it before it affects patient care.

Question 3

What is ambient clinical documentation?

Accepted Answer

Ambient clinical documentation is AI software that listens to a patient-clinician conversation during a visit and drafts a structured clinical note for the physician to review and sign, reducing manual note-taking during or after the appointment. See Ambient Clinical Documentation for the full definition.

Question 4

Is patient data used by clinical AI tools HIPAA-compliant?

Accepted Answer

HIPAA compliance depends on how a specific vendor handles patient data, not on the fact that a tool uses AI. Health systems typically require a Business Associate Agreement (BAA) with any AI vendor that accesses protected health information, along with documented data handling, storage, and retention practices. This is a question to verify directly with each vendor during procurement, not something this site can confirm on a vendor's behalf.

Question 5

How is clinical AI software validated before adoption?

Accepted Answer

Validation typically combines the vendor's own published performance data (such as sensitivity and specificity from clinical studies) with independent, site-specific testing on a health system's own patient population and equipment, since performance can vary by setting. Ongoing monitoring after go-live is also standard practice, since model performance can decline over time. See Clinical Validation and How Hospitals Evaluate Clinical AI Vendors.

Question 6

What is the difference between AI diagnostics and clinical decision support?

Accepted Answer

AI diagnostics refers to software that detects, classifies, or flags a specific finding, such as a suspicious lesion on an image. Clinical decision support (CDS) is a broader category: software that gives a clinician patient-specific guidance at the point of care, which can include diagnostic flags but also covers things like treatment suggestions, risk scores, and alerts. Most diagnostic AI tools are a type of CDS. See AI Diagnostics and Clinical Decision Support.

Question 7

Do hospitals need FDA clearance to use AI software?

Accepted Answer

Whether FDA clearance is required depends on how the software is classified and marketed. Software that meets the FDA's definition of a medical device, generally because it is intended to diagnose, treat, or otherwise directly inform a clinical decision, typically needs clearance or another applicable regulatory pathway before it can be marketed for that use in the U.S. Software used purely for administrative or operational purposes, such as scheduling, is usually outside that requirement. See Software as a Medical Device (SaMD).

Question 8

What is a false positive or false negative in AI diagnostics?

Accepted Answer

A false positive is a case where an AI tool flags a finding that is not actually present; a false negative is a case where the AI misses a finding that is actually present. These are captured by a tool's sensitivity and specificity statistics, and the acceptable balance between them depends on the clinical use case: a screening tool for a serious, treatable condition is often tuned to minimize false negatives even if that means more false positives to review.

Question 9

How do health systems evaluate AI vendors?

Accepted Answer

Common evaluation criteria include clinical evidence and validation data, regulatory status, data handling and security practices, EHR and workflow integration, post-deployment monitoring and model update processes, and total cost including implementation and training. See How Hospitals Evaluate Clinical AI Vendors and the Medical AI Vendors directory.

Question 10

What is model drift and why does it matter clinically?

Accepted Answer

Model drift is the gradual decline in an AI model's accuracy as real-world data, such as patient population or imaging equipment, shifts away from what the model was originally trained on. It matters clinically because a tool that was accurate at go-live is not guaranteed to stay accurate indefinitely, which is why ongoing monitoring, rather than one-time validation, is considered standard practice. See Model Drift.

Question 11

What is Software as a Medical Device (SaMD)?

Accepted Answer

Software as a Medical Device (SaMD) is software intended for a medical purpose, such as diagnosis or treatment support, that performs that purpose on its own rather than as part of a hardware device. Most standalone clinical AI diagnostic and decision-support tools fall into this category, and it is a key concept in how regulators classify and review clinical AI software. See Software as a Medical Device (SaMD).

Question 12

Are AI medical scribes accurate?

Accepted Answer

Accuracy varies by vendor, specialty, and visit complexity, which is why AI-drafted notes are treated as a starting draft rather than a final record: the clinician reviews and edits the note before signing it. Health systems piloting an AI scribe typically track edit rates and clinician time saved as part of evaluating a specific vendor's accuracy in their own setting.

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