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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Clinical decision support (CDS) and predictive AI are not mutually exclusive categories. CDS describes a software function that helps inform care decisions; predictive AI describes a way of deriving outputs from data. A predictive model can therefore be part of a CDS function. Hospitals comparing systems should look past the labels and assess each function’s intended use, inputs, output, workflow, evidence, human oversight, regulatory status, and lifecycle governance.
What is the difference between CDS and predictive AI?
The terms answer different questions. CDS asks what role a software function plays in care. Predictive AI asks how a model produces an output. A system that predicts a patient-specific risk and presents it to a clinician to inform a decision may be both predictive AI and CDS.
The U.S. Food and Drug Administration (FDA) defines CDS as software that provides health professionals or patients with health knowledge and person-specific information, intelligently filtered or presented at appropriate times to enhance health and health care. The FDA FAQ describes predictive decision support interventions (DSIs) as technology using algorithms or models derived from training or example data to produce outputs such as predictions, classifications, recommendations, evaluations, or analyses. The FAQ notes that some predictive DSIs may be devices under the Federal Food, Drug, and Cosmetic Act and some may not. The labels alone do not settle regulatory status. FDA CDS policy navigator; FDA CDS FAQ.
Compare the specific software functions, not only the product as a whole. One product can contain multiple functions, including functions that may qualify as non-device CDS and other functions that may be subject to device oversight. A vendor’s use of “AI,” “CDS,” “predictive DSI,” or “FDA-cleared” should prompt questions about the function and intended purpose rather than serve as a substitute for those answers.
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What should a hospital compare?
Use the following questions to structure procurement, clinical review, and implementation planning. The FDA recommends clarity about intended use, users, patient population, inputs, algorithm development and validation, and the information a clinician needs to evaluate the output. Applying those questions to the hospital’s own setting helps reveal whether the evidence and workflow fit the proposed use.
| Comparison area | Questions to ask | Why it matters |
|---|---|---|
| Intended use and users | Which decision is the function meant to inform? Who is the intended user, and which patient population and care setting are in scope? | A function’s purpose, intended users, and population matter to both clinical fit and regulatory assessment. FDA policy navigator. |
| Inputs and data quality | Which patient data are required, where do they come from, how often are they refreshed, and how are they checked? What happens if data are missing, delayed, or outside expected ranges? | Input relevance, collection instructions, and data-quality requirements affect whether the output can be interpreted in the intended use. FDA policy navigator. |
| Output and actionability | Does the system surface relevant information, present options, generate a score, issue an alert, or direct a specific action? | Output type is relevant to the FDA’s non-device CDS analysis. Recommendations and contextual information are examples that can meet one criterion; specific diagnostic or treatment directives and disease-specific risk scores are examples that do not meet that criterion. That criterion is only part of the full analysis. FDA policy navigator. |
| Urgency and workflow | When and where does the output appear? Is the decision time-critical, and how much time does the clinician have to examine its basis before acting? | The FDA says time-critical decision-support functions generally cannot meet all non-device CDS criteria. By contrast, retrieving contextual patient information in an emergency department may still qualify; the setting alone does not decide the classification. FDA CDS FAQ. |
| Evidence and local applicability | What data and methods were used to develop and validate the model? What clinical-validation results are available? Do the studied patients, care setting, and use match the hospital’s intended use? | The FDA identifies development methods, validation results, and patient-specific knowns and unknowns as information that can help clinicians independently review a recommendation’s basis. Matching that evidence to local use is an essential procurement question, not a guarantee of performance. FDA policy navigator. |
| Human oversight | Can clinicians inspect the basis for the output and apply their own judgment? Can they override it, defer action, or escalate a concern, and are those paths workable in the actual workflow? | Independent review and whether clinicians are intended to rely primarily on the recommendation are relevant to the statutory criteria for non-device CDS. FDA policy navigator. |
| Regulation and accountability | What is the regulatory status of each function in each applicable jurisdiction? Who is responsible for changes, safety reporting, and communicating updates? | Predictive DSI status alone does not determine whether a function is a device. The FDA also cautions that its CDS guidance is not the only potentially relevant digital-health policy. FDA CDS FAQ. |
| Lifecycle governance | Who monitors performance and incidents after deployment, reviews changes, informs affected teams, and decides whether use should be adjusted? | AI risk management extends across design, development, use, and evaluation. NIST’s AI Risk Management Framework is voluntary; WHO guidance emphasizes ethics, human rights, and accountability involving stakeholders. NIST AI RMF; WHO guidance. |
How do output and urgency affect the comparison?
A useful distinction is whether a function helps a clinician consider information or instead supplies a specific, time-sensitive direction. The FDA’s policy navigator treats recommendations and contextual information as examples that can meet one non-device CDS criterion, while identifying specific diagnostic or treatment directives, time-critical alarms, and disease-specific risk scores as examples that do not meet that criterion. This is not a standalone classification test: the other criteria and the function’s circumstances still matter.
Time pressure also affects whether a clinician can meaningfully review the basis of an output. A score presented during a decision that must be made immediately raises different questions from information retrieved for later review. But urgency or location by itself is not conclusive: the FDA FAQ notes that contextual patient information retrieval in an emergency department may still qualify as non-device CDS. FDA policy navigator; FDA CDS FAQ.
What evidence should vendors make inspectable?
Ask for enough information to determine what the function is intended to do, how it reaches its output, and when that output may not apply. The FDA recommends that software or labeling explain the intended use, users and patient population, required inputs and data-quality needs, algorithm development and validation, clinical-validation results, and patient-specific knowns and unknowns so clinicians can independently review the recommendation’s basis. FDA policy navigator.
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- Development and validation: Request the methods and data used, the validation approach, and available clinical-validation results. Ask what populations and settings those results cover.
- Input handling: Clarify required variables, acceptable data quality, how inputs are collected, and how the function signals missing or unsuitable data.
- Output interpretation: Ask what the output means, what it does not establish, and which patient-specific factors may make it less applicable.
- Independent review: Determine whether the clinician can see the relevant basis in time to assess the output rather than being asked to accept an unexplained recommendation.
These questions establish what a hospital can inspect; they do not establish that one system is more accurate, safer, or clinically useful than another. The official sources covered here do not provide head-to-head performance evidence for particular products, clinical areas, or local patient populations.
How should hospitals understand U.S. FDA status?
The FDA’s final Clinical Decision Support Software Guidance for Industry and Food and Drug Administration Staff, dated January 2026, interprets the statutory criteria in section 520(o)(1)(E) of the FD&C Act for certain software functions excluded from the device definition. The FDA policy navigator describes four criteria for non-device CDS functions:
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- The function does not acquire, process, or analyze certain medical images or signals.
- It displays, analyzes, or prints relevant medical information.
- It provides recommendations to health professionals about prevention, diagnosis, or treatment.
- It enables independent review of the recommendation’s basis so the clinician is not intended to rely primarily on the recommendation.
These criteria are a U.S.-specific regulatory framework, not a general definition of every clinical AI product. Whether a function meets them depends on the function and applicable circumstances; other functions may remain subject to FDA oversight. The FDA FAQ says predictive DSI is not, by itself, a determination of device status, and FDA cautions that the CDS guidance should not be the sole reference where other digital-health policies may apply. This summary is not legal advice or a global regulatory map. FDA January 2026 final guidance; FDA policy navigator; FDA CDS FAQ.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What governance is needed after procurement?
Procurement is not the end of the review. Identify who will monitor the deployed function, handle incidents, review changes, and decide whether the hospital should modify or pause its use. Make those responsibilities explicit across clinical teams, technical owners, and the supplier so an update or newly observed problem has a clear route for assessment.
NIST’s AI Risk Management Framework is a voluntary framework intended to incorporate trustworthiness considerations throughout AI design, development, use, and evaluation. WHO’s health AI guidance places ethics and human rights at the center of design, deployment, and use and emphasizes stakeholder accountability. These sources provide governance principles, not product-specific performance findings or a standardized hospital procurement scorecard. NIST AI RMF; WHO guidance.
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