Below is a detailed comparison of OPTION D ONLY against the published/public 2025 Appendix S you provided. I am treating the “purpose” of the revisions as inferred from the clean Option D text, since we do not have a marked-up author memo.
Bottom line
Public Appendix S is a compact, three-part taxonomy: Assistive = detects data; Augmentative = analyzes/quantifies data; Autonomous = interprets data and independently generates conclusions. It is short, but leaves many borderline cases unresolved, especially where software produces a score, risk category, likelihood statement, or recommendation.
Option D appears designed to make the taxonomy more operational and harder to game. It shifts the focus from generic “work performed by the machine” to the software output(s) and the role of those outputs in clinical care. It adds explicit requirements around derived parameters, clinical meaningfulness, validity, patient-management contribution, and the distinction between augmentative parameters and autonomous interpretations/conclusions. It also weakens the old bright-line rule that augmentative software always requires physician/QHP interpretation and report.
1. Overall purpose: from a simple AI taxonomy to a coding-descriptor decision framework
The published Appendix S says the taxonomy provides guidance for classifying AI applications into assistive, augmentative, and autonomous, and expressly says “AI” is not defined in the code set because the term alone is not sufficient to describe clinical use or utility. It classifies services based on the clinical procedure and the “work performed by the machine” on behalf of the physician/QHP.
Option D keeps that basic purpose but makes it more CPT-operational. It says the taxonomy provides guidance in determining the appropriate terminology for CPT code descriptors, not merely for classifying AI applications. It also says classification depends on the software output(s) and their role in clinical care.
That is a significant change in orientation. Public Appendix S is more like a conceptual taxonomy. Option D is more like a coding-language control document: what should the CPT descriptor call this software-mediated service?
Result: Option D makes the Appendix less about “what kind of AI is this?” and more about “what kind of clinically usable output is being generated, and what descriptor term is justified?”
2. Option D adds a threshold requirement: the software output must be clinically useful
The public version says the taxonomy applies to AI in medical services and procedures, but it does not add a strong gatekeeping sentence about medical-purpose usefulness.
Option D adds that, in CPT, the AI software outputs must be useful in the diagnosis, cure, mitigation, treatment, or prevention of disease or other conditions.
This is important because it excludes or de-emphasizes software outputs that are merely technical, administrative, convenience-oriented, or workflow-supportive unless they have a clinical role.
Result: Option D narrows the meaningful universe of Appendix S. It says the relevant object is not “AI software” in the abstract, but clinically useful software output.
3. Option D adds a CPT application/validity requirement
This is one of the strongest additions. Option D says assignment of an Appendix S term requires that the service or procedure demonstrate validity applicable to the specific term included in the code descriptor through the appropriate CPT application process, such as Category I or Category III.
The public Appendix S does not state this with comparable explicitness. It classifies the work, but does not clearly say that the label “assistive,” “augmentative,” or “autonomous” must itself be supported by validity evidence tied to the descriptor.
Purpose: This appears intended to prevent applicants from casually claiming “augmentative” or “autonomous” status merely because the product uses AI or produces a complex-looking output.
Result: Option D turns the taxonomy into an evidentiary standard. A software service does not merely “sound augmentative”; it must show validity appropriate to the claimed category.
4. Assistive: from “detects data” to “provides clinically relevant data without derived parameters or conclusions”
Public Appendix S
The public version defines assistive software as machine work that detects clinically relevant data without analysis or generated conclusions, requiring physician/QHP interpretation and report.
Option D
Option D defines assistive software as software whose outputs provide clinically relevant data without deriving a quantitative or categorical parameter — such as an index, score, or classification — and without generating an interpretation or conclusion.
Option D then expands the assistive category by saying assistive output is clinically supportive and may improve physician/QHP performance — accuracy, precision, inter-observer variability — even if the output of the primary service is unchanged.
Why this matters
The public version’s phrase “without analysis” is potentially too blunt. Many “assistive” tools do some computational work — detection, segmentation, highlighting, flagging, image enhancement — but may not create a new clinical parameter or conclusion. Option D’s distinction is more precise: the issue is not whether the machine did computation, but whether the output crosses into a derived parameter or interpretive conclusion.
Result: Option D may keep some computer-assisted detection or highlighting tools in Assistive, even if they involve substantial algorithmic processing, so long as they do not produce a score, classification, interpretation, or conclusion.
5. Assistive now oddly includes “likelihood of,” “suggestive of,” and “risk for”
Option D states that assistive outputs may include terms such as “likelihood of,” “suggestive of,” or “risk for.”
This is a notable and potentially controversial shift. In the public version, “likelihood of pathophysiology” appears in the autonomous discussion as an example of a clinically meaningful conclusion used to establish a diagnosis or intervention.
Purpose: Option D may be trying to recognize that not every “likelihood” or “risk” phrase is autonomous. A tool might flag an area as “suggestive of X” while still requiring physician interpretation and report.
Result: The same words — “likelihood,” “suggestive,” “risk” — no longer automatically imply autonomy. The classification depends on whether the output is merely clinically relevant/supportive data or whether it becomes a derived parameter, definitive interpretation, diagnosis, or management conclusion.
This is clarifying in one sense, but it also creates a new ambiguity: a “risk for” output can sound like a risk score, which Option D elsewhere places under augmentative outputs. The practical distinction would need to rest on whether the output is a true quantitative/categorical parameter versus a supportive detection-like statement.
6. Augmentative: major rewrite from “analyzes/quantifies” to “derived parameter distinct from input”
Public Appendix S
The public version defines augmentative machine work as analyzing and/or quantifying data to yield clinically meaningful output, requiring physician/QHP interpretation and report.
Option D
Option D defines augmentative software as output that represents a quantitative or categorical parameter that is qualitatively different than the input. It must be more than a summation of data inputs — more than adding, averaging, measuring, or reporting descriptive statistics.
It then gives examples: clinical scales, indexes, categorical classifications, risk scores, or other metrics that may be used in diagnosis, cure, mitigation, treatment, or prevention.
Why this matters
This is probably the biggest conceptual revision in Option D. Public Appendix S could treat almost any analytic or quantitative software output as augmentative. Option D says: not so fast. A measurement, average, or descriptive statistic is not enough. The output must be a clinically meaningful derived parameter that is distinct from the input.
Result: Option D likely reduces overclassification as augmentative. Simple measurement tools, calculators, summation tools, or descriptive analytics would not automatically become augmentative merely because they quantify data.
7. Option D adds validation expectations for augmentative outputs
Option D adds that an augmentative output should be validated by equivalence to a metric currently in clinical use. If novel, with no such existing metric, it should be validated for impact on patient management, such as novel predictive or prognostic indices.
This is absent from the public version’s shorter augmentative definition.
Purpose: This seems designed to stop a proliferation of black-box scores that are “clinically meaningful” only by assertion.
Result: For a novel AI-derived score, Option D asks: Is this equivalent to something clinicians already use? If not, is there evidence it changes patient management? That is a much higher and more concrete bar.
8. Option D explicitly distinguishes augmentative from autonomous by excluding definitive interpretation/conclusion
Option D says software is classified as augmentative if the output does not include a definitive interpretation or conclusion as would be required for autonomous.
The public version’s boundary is less explicit: augmentative analyzes/quantifies; autonomous interprets and independently generates conclusions.
Purpose: Option D is trying to make the boundary less dependent on the vague verb “analyzes” and more dependent on the output’s clinical role.
Result: A risk score, index, scale, or classification may be augmentative if it supports physician decision-making. But once the software independently generates a definitive diagnosis, specific management recommendation, or intervention-level conclusion, it moves toward autonomous.
9. Clinical meaningfulness becomes a defined concept
Option D repeatedly uses “clinically meaningful” and then defines it. For augmentative outputs, it says they are clinically meaningful based on documentation that the output contributes to patient management.
It also includes a key definition: software outputs are clinically meaningful based on documentation that the output contributes to patient management.
The public version uses “clinically meaningful output” and “clinically meaningful conclusions,” but does not define the term as tightly in the provided text.
Result: Option D makes “clinically meaningful” less rhetorical. The output must be tied to patient management, not merely statistically significant, technically impressive, or biologically interesting.
10. Augmentative no longer always requires physician/QHP interpretation and report
This is a major practical change.
In the public table, augmentative software requires physician/QHP interpretation and report: Yes.
In Option D, the table says the output requires physician/QHP interpretation and report “Conditionally” for augmentative, with a note that physician work related to augmentative services may be captured by existing codes.
Option D also states in the body that software with augmentative outputs may or may not require physician/QHP work through interaction with the software, such as adjustment of software settings based on clinical context.
And it adds that physician work related to augmentative services is typically captured by existing codes, such as when the output is a data element in E/M, a factor in pre-surgical planning, or integrated into a separate service with physician/QHP interpretation.
Purpose: Option D seems to be acknowledging real-world coding. Many augmentative outputs are not separately interpreted and reported as standalone physician work; they are incorporated into another service.
Result: This change may reduce the pressure to create separate CPT codes for every augmentative software output. It also clarifies that the software output may be part of a broader service rather than a separately reportable AI act.
11. Autonomous: from “interprets data and independently generates conclusions” to “derives parameters and generates interpretations”
Public Appendix S
The public version defines autonomous machine work as automatically interpreting data and independently generating clinically meaningful conclusions without concurrent physician/QHP involvement. It says autonomous services include interrogating and analyzing data and may include acquisition, preparation, and/or transmission.
Option D
Option D defines autonomous software as software that automatically, without concurrent physician/QHP involvement, derives parameters and independently generates clinically meaningful interpretations.
It then says reporting the derived parameters is essential for transparency and explainability at all autonomous levels.
Purpose: Option D appears to build autonomy on top of the same concept used for augmentative software: derived parameters. Autonomous software does not merely “interpret data”; it derives parameters and then generates interpretations/conclusions from them.
Result: Autonomous becomes more structurally defined. It is not just “AI made a conclusion.” It is: input → derived parameter(s) → independent clinical interpretation/conclusion/recommendation/action.
12. Option D adds transparency and explainability requirements for autonomous outputs
Option D states that reporting derived parameters is essential for transparency and explainability at all autonomous levels.
The public version does not include this explicit transparency/explainability requirement in the provided text.
Purpose: This likely responds to concerns that autonomous AI decisions need traceable intermediate outputs, not just black-box conclusions.
Result: Option D makes autonomous status harder to justify if the software cannot disclose or report the derived parameters underlying its recommendation or action.
13. Option D strengthens autonomous validity and utility language
Option D says recommendations for definitive diagnostic conclusions or specific management/interventions should provide clinical utility and have demonstrated validity. It adds that this may include placing results in the context of epidemiologic data or clinical practice guidelines, especially for Levels II and III.
The public version describes autonomous output as clinically meaningful conclusions that may be used to establish a diagnosis or implement an intervention, but Option D adds more explicit proof expectations.
Result: Option D raises the bar most sharply for Level II and Level III autonomy, where software actions can proceed with override opportunity or ongoing physician oversight rather than immediate physician implementation.
14. Autonomous Level I: changed from “contestable options” to “recommendations requiring physician judgment”
The public version says Level I autonomous AI draws conclusions and offers diagnosis and/or management options that are contestable and require physician/QHP action to implement.
Option D says Level I output includes recommendations of definitive diagnostic and/or specific management or interventions based on derived parameters, requiring physician/QHP judgment to implement or reject.
Purpose: Option D makes Level I more explicitly recommendation-based and less dependent on the somewhat slippery word “contestable.”
Result: The Level I category becomes a software-generated clinical recommendation that still requires an affirmative professional decision.
15. Autonomous Level II: refined around impending action and opportunity to negate
The public version says Level II draws conclusions and initiates diagnosis and/or management options with alert/opportunity for override, which may require physician/QHP action to implement.
Option D says Level II output includes medical management actions based on interpretations and conclusions from derived parameters, and the software must allow a reasonable opportunity to negate the impending action before implementation, such as by alert.
Purpose: Option D clarifies that Level II is not merely “offers options with override.” It is closer to “the system is about to act, but the clinician gets a chance to stop it.”
Result: Level II becomes a more specific automation category: software-initiated management with a pre-implementation override window.
16. Autonomous Level III: refined around automatic ongoing management with oversight
The public version says Level III draws conclusions and initiates management, requiring physician/QHP initiative to contest.
Option D says Level III automatically initiates management actions based on interpretations and conclusions from derived parameters. These actions require physician/QHP oversight and performance review and continue unless the physician/QHP intervenes. It adds that oversight is typically over multiple interventions to determine whether management goals are achieved.
Purpose: Option D is trying to make Level III less like a single autopilot event and more like a continuing autonomous management function.
Result: Level III is narrowed to systems that automatically initiate and continue management actions unless stopped, with clinician oversight of performance over time.
17. Option D adds key definitions that public Appendix S lacks or leaves implicit
Option D adds three explicit definitions:
Derived parameters are quantitative or categorical software outputs, such as an index, score, or classification.
Clinically meaningful means documented contribution to patient management.
Automated/automatically refers to algorithmic work from input to output — deriving parameters — and can apply to either augmentative or autonomous software.
This last point is very important. In public Appendix S, “automatically” appears mainly in the autonomous definition. Option D clarifies that automatic derivation of parameters does not make software autonomous. Automation can occur in augmentative software too.
Result: This is one of Option D’s most useful corrections. It separates automation from autonomy. A tool can automatically generate a risk score and still be augmentative if it does not independently generate a definitive interpretation, diagnosis, management recommendation, or action.
18. Table changes: the taxonomy becomes more output-centered
The public table says:
Assistive: detects clinically relevant data.
Augmentative: analyzes/quantifies data to yield clinically meaningful output.
Autonomous: interprets data and independently generates clinically meaningful conclusions.
Option D’s table changes the axis:
Assistive: clinically supportive data, including “likelihood of,” “suggestive of,” or “risk for.”
Augmentative: derives clinically meaningful parameters distinct from input and identifying a particular clinical condition, including “predictive of” or “prognostic of.”
Autonomous: derives clinically meaningful parameters and from them generates interpretations or conclusions in the form of diagnosis or intervention.
Result: Option D replaces the old “detects / analyzes / interprets” ladder with a more nuanced ladder:
data support → derived clinical parameter → derived parameter plus independent interpretation/conclusion/action.
That is the central logic of Option D.
19. Practical classification consequences
Here is how Option D would likely change real-world classification outcomes:
Simple detection/highlighting tools remain assistive, even if algorithmically sophisticated, if they do not generate a derived parameter or conclusion.
Simple measurement/quantification tools may not qualify as augmentative if they merely add, average, measure, or report descriptive statistics. Option D expressly says augmentative output must be more than that.
Risk scores, indexes, clinical scales, and categorical classifications are more clearly augmentative, provided they are clinically meaningful, distinct from inputs, and documented to contribute to patient management.
Predictive or prognostic indices can be augmentative, but if novel, they require validation for patient-management impact.
Software recommendations for definitive diagnosis or specific intervention are pushed toward autonomous Level I, if physician/QHP judgment is required to implement or reject.
Software that initiates actions unless stopped becomes autonomous Level II or Level III, depending on whether there is a pre-action opportunity to negate or ongoing automatic management that continues unless the physician intervenes.
20. The most important conceptual result
The published Appendix S uses three verbs that feel intuitive but imprecise:
Detects → analyzes/quantifies → interprets/concludes.
Option D substitutes a more coding-usable structure:
Clinically relevant data → clinically meaningful derived parameter → derived parameter plus independent interpretation/conclusion/action.
That is a meaningful improvement. It gives CPT applicants and reviewers more handles: Is there a derived parameter? Is it qualitatively different from the input? Is it clinically meaningful? Is it validated? Does it contribute to patient management? Does it merely support the physician, or does it independently interpret, recommend, or act?
21. Residual problems in Option D
Option D is clearer, but not perfectly clean.
First, the assistive examples “likelihood of,” “suggestive of,” and “risk for” may blur into augmentative or autonomous territory, depending on how the output is generated and reported.
Second, Option D says augmentative output may identify a particular clinical condition, including “predictive of” or “prognostic of,” while autonomous output generates interpretations or conclusions in the form of diagnosis or intervention. The distinction between “identifies a clinical condition” and “generates a diagnosis” may still be difficult in practice.
Third, Option D imports evidentiary standards — validity, clinical utility, patient-management contribution — into a nomenclature appendix. That is probably intentional, but it may create friction because CPT taxonomy, FDA validation, clinical utility, and payer coverage are adjacent but not identical concepts.
Fourth, Option D’s statement that physician work related to augmentative services is typically captured by existing codes may be useful but could also be read as discouraging separate coding for many software outputs.
Concise executive summary
Option D is not merely a wordsmithing revision of Appendix S. It changes the taxonomy from a broad “machine work” classification into an output-based CPT descriptor framework. Its main purpose is to clarify that the relevant object is the clinical software output, not the AI product. Its main result is a sharper boundary between assistive data, augmentative derived parameters, and autonomous interpretation or action. It also raises the evidentiary bar by requiring validity, clinical meaningfulness, patient-management contribution, and — for autonomous tools — transparency/explainability of derived parameters. The biggest practical change is that automatic generation of a score is not autonomous; automation can be augmentative. Autonomy begins when the system independently generates a clinically meaningful interpretation, recommendation, diagnosis, or management action.
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