report.object
AI models for clinical diagnostics and imaging
Expert-reviewed from public records, platform data, primary sources, and private research beyond AI search. Use /api/v1/reports/RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING/sample before purchase, then download PDF, JSON, or Markdown after checkout.
page.markdown
AI models for clinical diagnostics and imaging.md
# AI models for clinical diagnostics and imaging
Diagnostics and imaging models should be evaluated by accountable follow-up: authorised use, local case mix, equipment, reader behaviour, alert burden, monitoring, update rights and follow-up capacity.
## Object
| field | value |
| ---------------- | ----------------------------------------------------- |
| report_id | RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING |
| schema_version | 2026-05-28 |
| last_updated_at | 2026-06-02 |
| category | MODELS |
| sector | Healthcare delivery |
| region | GLOBAL |
| confidence_score | 0.87 |
| source_count | 27 |
| evidence_count | 30 |
## Endpoints
| action | endpoint |
| ------------ | -------------------------------------------------------------------------------- |
| summary | /api/v1/reports/RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING |
| sample | /api/v1/reports/RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING/sample |
| schema | /api/v1/reports/RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING/schema |
| quote | /api/v1/reports/RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING/quote |
| purchase | /checkout/RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING?view=agent |
| versions | /api/v1/reports/RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING/versions |
| changelog | /api/v1/reports/RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING/changelog |
| monitor | /api/v1/monitors?report_id=RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING |
| mcp_resource | colophon://reports/RPT_HEALTHCARE_DELIVERY_AI_MODELS_DIAGNOSTICS_IMAGING/sample |
## Coverage
| field | value |
| -------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| category | MODELS |
| sector | Healthcare delivery |
| region | GLOBAL |
| topics | clinical diagnostics, medical imaging AI, FDA-authorized AI devices, radiology AI, screening AI, SaMD, GMLP, reader workflow, model monitoring, clinical validation |
| source_classes | device_registry, device_guidance, regulatory_principles, clinical_society, clinical_review, vendor_docs, regulatory_guidance, regulatory_roadmap, academic_reporting_guideline, clinical_trial, systematic_review, health_technology_assessment, vendor_directory |
| update_cadence | Weekly |
## Sample JSON
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"Published model performance rarely captures downstream worklist effects, false-positive cost and clinical accountability."
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"title": "AI models for clinical diagnostics and imaging",
"subtitle": "The scarce asset is accountable follow-up, not detection.",
"abstract": "Diagnostics and imaging models should be evaluated by accountable follow-up: authorised use, local case mix, equipment, reader behaviour, alert burden, monitoring, update rights and follow-up capacity.",
"category": "MODELS",
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"publicationDate": "2025-08-01",
"transformChain": [],
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"url": "https://www.fda.gov/regulatory-information/search-fda-guidance-documents/marketing-submission-recommendations-predetermined-change-control-plan-artificial-intelligence"
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"originalUrl": "https://health.ec.europa.eu/ehealth-digital-health-and-care/artificial-intelligence-healthcare_en",
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"url": "https://health.ec.europa.eu/ehealth-digital-health-and-care/artificial-intelligence-healthcare_en"
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{
"id": "E2",
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"status": "verified",
"confidence": 0.87,
"access": "sample"
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{
"id": "E3",
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"confidence": 0.86,
"access": "sample"
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{
"id": "E4",
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"confidence": 0.86,
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"status": "verified",
"confidence": 0.9,
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{
"id": "E13",
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"status": "verified",
"confidence": 0.88,
"access": "sample"
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{
"id": "E15",
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"summary": "STARD-AI provides diagnostic-accuracy reporting guidance for AI-based tests, including AI-specific items on data handling, bias, fairness, applicability and generalizability.",
"status": "verified",
"confidence": 0.86,
"access": "sample"
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{
"id": "E18",
"sourceId": "S18",
"claimIds": [],
"summary": "The MASAI mammography screening study reported higher cancer detection and reduced screen-reading workload in an AI-supported screening arm, making mammography a stronger scale candidate when screening-program context, recall policy and follow-up are controlled.",
"status": "verified",
"confidence": 0.88,
"access": "sample"
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{
"id": "E19",
"sourceId": "S19",
"claimIds": [],
"summary": "A Viz.ai LVO systematic review and meta-analysis found workflow-time improvements for stroke triage but did not show statistically significant mortality, functional-independence or length-of-stay differences.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
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{
"id": "E21",
"sourceId": "S21",
"claimIds": [],
"summary": "NIHR evidence synthesis for chest X-ray lung-cancer AI reports a sensitivity-specificity trade-off in one UK study and notes subscription, volume-tier and implementation pricing units.",
"status": "verified",
"confidence": 0.85,
"access": "sample"
}
],
"claims": [
{
"id": "C1",
"statement": "FDA authorization records make authorized diagnostic and imaging AI device claims searchable by intended use and product record.",
"confidence": 0.88,
"evidenceIds": [
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{
"id": "C2",
"statement": "FDA authorization is necessary evidence for regulated diagnostic use, but it does not prove local case mix, equipment fit, reader workload, follow-up capacity or accepted-output economics.",
"confidence": 0.88,
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"E2",
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"colophon": {
"checkedAt": "2026-06-02",
"latestRevisionAt": "2026-06-02T00:00:00Z",
"reviewStandard": "expert_human_reviewed",
"kinetics": {
"speed": "moderate",
"impact": 0.91,
"momentum": 0.82,
"businessImpact": "Diagnostics AI depends on finding-to-action capacity: authorized detections create value only when follow-up work absorbs them.",
"rateOfChange": "Local validation evidence, PCCP-covered model changes, jurisdictional AI rules and follow-up telemetry can change shortlist and retest obligations before capital cycles.",
"from": "clearance logos and modality pilots",
"toward": "finding-to-action paths with follow-up capacity, drift monitoring and version control"
},
"watch": [],
"gapAnalysis": "Longitudinal site-level diagnostics AI telemetry linking authorised use, local case mix, scanner or assay protocol, reader behaviour, false-positive workload, escalation ownership, follow-up completion and patient outcome."
},
"body": {
"sections": [
{
"id": "executive-summary",
"title": "Executive summary",
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"id": "executive-summary-narrative",
"evidenceIds": [
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"type": "section_narrative",
"lead": "Diagnostics AI pays only when the service can absorb the extra findings: local readers, alert queues and follow-up teams must prove capacity before a model moves into routine care.",
"body": [
"Regulatory authorization makes diagnostic models searchable. It does not make the diagnostic service ready for the extra findings the model creates.",
"FDA records make the authorized indication searchable [E1]. PCCP guidance means a cleared model may also have a pre-specified change pathway, so the contract has to expose installed version, covered changes, validation package and local retest trigger [E12].",
"The trap is buying detection into a capacity-constrained service. A model can raise sensitivity while burying radiologists, screening clinicians or care teams in false positives, overrides and unfinished follow-up; chest-X-ray evidence and broader efficiency reviews show why time and sensitivity claims need local proof [E21, E22].",
"The first proof object should be a local reader and follow-up study: authorized use, local validation, reader response, follow-up completion, alert burden, monitoring fields and version-change retest rights. Mammography screening and LVO stroke triage each have named public evidence with different endpoints [E18, E19]. Autonomous diabetic-retinopathy screening adds a point-of-care screening lane with its own gradability and referral caveats [E20]."
],
"detail": [
"Shortlist models with authorization and clinical evidence, but approve production only by service-line capacity and local reader workflow.",
"Require vendors to show how updates, thresholds and monitoring reopen validation before the model becomes part of routine care."
]
},
{
"id": "board-decision-table",
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"question": "What is the object of approval?",
"answer": "A local reader and follow-up study for a named modality, disease class, workflow and follow-up owner.",
"move": "Diagnostic service, procurement and quality leaders should require local validation and follow-up telemetry before routine-care deployment."
},
{
"question": "Which lane goes first?",
"answer": "The indication where faster detection changes treatment timing and the service can absorb extra findings.",
"move": "Baseline turnaround, false positives, overrides, follow-up completion and alert burden."
},
{
"question": "What remains blocked?",
"answer": "Generalizing one authorization, scanner mix, population, threshold or reader workflow to another.",
"move": "Retest when indication, equipment, population, workflow or model version changes."
}
]
}
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},
{
"id": "what-changed",
"title": "What changed",
"access": "sample",
"dek": "Public evidence is now structured enough to define private approval tests.",
"claimIds": [
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"type": "section_narrative",
"lead": "Diagnostic AI now has enough authorization and validation evidence for serious buying, but local follow-up capacity is the gating question.",
"body": [
"Authorization records make intended use and product claims searchable [E1], but the value leak happens after detection. Scanners, acquisition protocols, prevalence, reader behaviour, false-positive burden and follow-up capacity decide whether a model shortens time to care or creates alert debt [E8, E21].",
"Build a service-line scorecard before procurement: authorized intended use, jurisdiction, PCCP status, input data, reader role, action owner, follow-up capacity, monitoring fields, model-change rights and stop-or-retest thresholds.",
"For high-risk workflows, the approval record has to connect source evidence, accepted output, reviewer action, exception path, outcome signal and retained audit record before volume or staffing claims matter."
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{
"id": "trigger-table",
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"trigger": "FDA AI-enabled device list",
"why_now": "A public registry makes authorized diagnostic AI searchable by device and specialty [E1].",
"buyer_action": "Use authorization as the first screen, then test indication-to-action fit."
},
{
"trigger": "AI-enabled device software guidance",
"why_now": "FDA AI-enabled-device materials and PCCP guidance raise the bar for model-change evidence and marketing-submission terms [E2, E12].",
"buyer_action": "Ask vendors for installed-version, monitoring, update, drift, disclosure, rollback and retest commitments."
},
{
"trigger": "EU and UK AI medical-device rules",
"why_now": "EU high-risk AI and MHRA SaMD/AI work add human-oversight, data, cybersecurity and post-market evidence obligations beyond FDA identifiers [E13, E14].",
"buyer_action": "Require jurisdiction-specific control artefacts before cross-border rollout."
},
{
"trigger": "Clinical workflow integration",
"why_now": "Detection value depends on reader, worklist and care-team action [E8, E21].",
"buyer_action": "Run private reader, alert-burden and follow-up-capacity studies before broad rollout."
}
]
}
]
},
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"id": "decision-map",
"title": "Decision map",
"access": "sample",
"dek": "Workflow approval changes by authority, source boundary and proof burden.",
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"E15",
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"lead": "The diagnostic action path matters more than the detection model: scale only the indications where extra findings can be read, escalated, followed up and monitored on local data.",
"body": [
"The decision map should start with an indication-to-action operating path with authorized use, local validation, reader response, follow-up owner, monitoring fields and version-change rights: what source record is read, what output is drafted, who approves it, where it is stored and what event forces reapproval.",
"A system can pass a low-risk support test and still fail production work: source evidence, accepted output, integration state, exception handling, reviewer burden and downstream rework have to hold together.",
"The useful vendor question is whether the system can make the named task inspectable, reversible and measurable inside the operating record."
]
},
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"title": "Workflow selection",
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"E19",
"E15",
"E18"
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"key": "route",
"label": "Workflow",
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{
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"key": "gate",
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"align": "left"
}
],
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"route": "Acute radiology triage",
"accepted_output": "Worklist prioritization or alert that a radiologist can accept, reject or escalate.",
"gate": "Authorized indication, modality, scanner protocol, threshold and alert owner are documented.",
"buyer_rule": "Approve when faster escalation does not create a false-positive queue, unresolved follow-up or unsupported outcome claim [E19]."
},
{
"route": "Diagnostic detection",
"accepted_output": "Model output used as reader support for a defined finding or disease class.",
"gate": "Local validation by population, equipment, prevalence, reader role and reporting-guideline fields [E15, E17].",
"buyer_rule": "Do not generalize from one indication, device class or reader workflow to another."
},
{
"route": "Screening programmes",
"accepted_output": "Mammography or diabetic-retinopathy screening result routed to recall, referral or routine follow-up.",
"gate": "Screening population, recall policy, image gradability, referral capacity and interval or follow-up monitoring are measured [E18, E20].",
"buyer_rule": "Approve by programme design and follow-up capacity, not by detection sensitivity alone."
},
{
"route": "Second-read screening support",
"accepted_output": "Review-prioritization, region-of-interest or screening-assist output.",
"gate": "Case mix, image quality, reader workflow and quality-control evidence should follow local validation and imaging-reporting checks [E8, E17].",
"buyer_rule": "Pilot where accountable review and discordance adjudication are available."
},
{
"route": "Care-team notification",
"accepted_output": "Routed alert or care-coordination task tied to a diagnostic finding.",
"gate": "Named recipient, escalation, patient-impact tracking and alert-fatigue monitoring should be tested against the vendor workflow shape [E9, E10].",
"buyer_rule": "Approve only when the care team can absorb the increased detection volume."
}
]
},
{
"id": "route-definition-callout",
"title": "Route definition",
"evidenceIds": [
"E19",
"E15",
"E18"
],
"type": "callout",
"tone": "neutral",
"body": "Route means the approved path from work to accepted output: the source set, data boundary, model or vendor channel, tools, reviewer, evidence record and reapproval trigger that make an AI result usable."
}
]
},
{
"id": "signature-proof",
"title": "Signature proof",
"access": "locked",
"dek": "The decisive proof is whether the workflow survives evidence, audit and rework.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "signature-proof-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "model-evidence-stack",
"title": "Model evidence stack",
"access": "locked",
"dek": "The model is only one layer in an accepted clinical, diagnostic or scientific output.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "model-evidence-stack-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "use-case-route-selection",
"title": "Workflow selection",
"access": "locked",
"dek": "Production starts with the lane where output can be accepted or rejected visibly.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "use-case-route-selection-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "evidence-and-benchmarks",
"title": "Evidence fit",
"access": "locked",
"dek": "Public evidence maps to local accepted work.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "evidence-and-benchmarks-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "private-eval-design",
"title": "Private eval design",
"access": "locked",
"dek": "Public evidence chooses the tests; local cases decide production.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "private-eval-design-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "risks-and-controls",
"title": "Risks and controls",
"access": "locked",
"dek": "The main risk is ungoverned action.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "risks-and-controls-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "operating-model",
"title": "Operating model",
"access": "locked",
"dek": "Production ownership crosses clinical, technical, compliance and finance teams.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "operating-model-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "economics",
"title": "Economics",
"access": "locked",
"dek": "Price the accepted work.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "economics-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "implementation-signals",
"title": "Public claim tests",
"access": "locked",
"dek": "Vendor pages seed diligence; local telemetry supplies operating proof.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "implementation-signals-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "vendor-directory",
"title": "Diligence directory",
"access": "locked",
"dek": "Representative vendors and platform RFI anchors to include in diligence.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "vendor-directory-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "recommended-next-steps",
"title": "Recommended next steps",
"access": "locked",
"dek": "A practical workflow can be approved in phases without pretending the whole market is ready.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "recommended-next-steps-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "known-unknowns",
"title": "Known unknowns",
"access": "locked",
"dek": "These gaps should shape diligence rather than delay all buying.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "known-unknowns-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "source-ledger",
"title": "Source ledger",
"access": "locked",
"dek": "Primary public sources and evidence rows behind the workflow analysis.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "source-ledger-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "glossary",
"title": "Glossary",
"access": "sample",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "glossary-table",
"title": "Terms used in this report",
"caption": "Definitions are reader aids, not separate evidence claims.",
"evidenceIds": [],
"type": "table",
"columns": [
{
"key": "term",
"label": "Term",
"align": "left"
},
{
"key": "meaning",
"label": "Plain meaning",
"align": "left"
}
],
"rows": [
{
"term": "Route",
"meaning": "The approved path from work to accepted output: source set, data boundary, model or vendor channel, tools, reviewer, evidence record and reapproval trigger."
}
]
}
]
}
]
}
},
"gap_analysis": "Longitudinal site-level diagnostics AI telemetry linking authorised use, local case mix, scanner or assay protocol, reader behaviour, false-positive workload, escalation ownership, follow-up completion and patient outcome.",
"kinetics": {
"speed": "moderate",
"impact": 0.91,
"momentum": 0.82,
"businessImpact": "Diagnostics AI depends on finding-to-action capacity: authorized detections create value only when follow-up work absorbs them.",
"rateOfChange": "Local validation evidence, PCCP-covered model changes, jurisdictional AI rules and follow-up telemetry can change shortlist and retest obligations before capital cycles.",
"from": "clearance logos and modality pilots",
"toward": "finding-to-action paths with follow-up capacity, drift monitoring and version control"
},
"watch_signals": [],
"sample_findings": [
"The buying bottleneck has moved from finding an authorized algorithm to proving that a diagnostic service can act on extra findings."
]
}
```