ScreenPoint Medical

ScreenPoint Medical is a Netherlands-based healthcare AI company, founded in 2014 by Professor Nico Karssemeijer and Professor Sir Michael Brady, that develops Transpara Breast AI for earlier detection of breast cancer in 2D (FFDM) and 3D (DBT) mammography. Transpara is vendor-agnostic, integrates into PACS and reading workflows via medical-imaging (DICOM) pipelines rather than a public developer API, and is FDA cleared (K241831, K232096) and CE marked (0344). The product is used in over 200 clinics across 25+ countries and is backed by three randomized controlled trials (including MASAI in The Lancet). The company is backed by Insight Partners. This profile was enriched by the API Evangelist pipeline: no public developer/API surface exists, but real security-disclosure and compliance posture were captured.

ScreenPoint Medical is profiled on the APIs.io network. Tagged areas include Company, Healthcare, Artificial Intelligence, Medical Imaging, and Mammography.

ScreenPoint Medical’s developer surface includes engineering blog and 8 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

13.6/100 emerging ▬ flat Agent 0/100 human only Front door AI 5/6 prominent Heartbeat 88/100 alive Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs

Company Healthcare Artificial Intelligence Medical ImagingMammographyBreast Cancer Radiology Machine Learning

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 13.6/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 0.4 / 17

Access Clarity 3.1 / 17

Operational Transparency 1.2 / 11

Contract Governance 1.9 / 10

Discoverability 4.1 / 9

Regulatory Posture 2.9 / 15

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Health regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes

13 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points
Developer Ergonomics scores 2.4 — up to +19.5 composite points
Access Clarity scores 18.4 — up to +16.3 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/screenpoint-medical/apis.yml: open an issue on github.com/api-evangelist/screenpoint-medical to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

Market	Position	Depth	Registered MCP server	Declares compatibility
Canada Medical Device Licensing (Health Canada MDALL licence holders, incl. SaMD)	regulated	62.2	-	-
FDA Device Establishments (radiology, nuclear medicine, cardiovascular, robotic surgery)	regulated	62.2	-	-

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Screenpoint Medical Domain Security

TLSv1.3 · HSTS · DNSSEC · DMARC

SECURITY

Screenpoint Medical Vulnerability Disclosure

contact published

SECURITY

Resources

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Conformance

Conformance

Access & Security 4

Authentication, authorization, and security posture

Security

Security

Compliance

Compliance

VulnerabilityDisclosure

VulnerabilityDisclosure

DomainSecurity

DomainSecurity

Commercial 1

Pricing, plans, and the legal terms of use

PrivacyPolicy

PrivacyPolicy

Company 3

The organization behind the API

BlogRSS

BlogRSS

Website

Website

Blog

Blog

Source (apis.yml)

aid: screenpoint-medical
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - security
  generated: '2026-11-06'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/screenpoint-medical.png
name: ScreenPoint Medical
description: 'ScreenPoint Medical is a Netherlands-based healthcare AI company, founded in 2014 by Professor Nico Karssemeijer
  and Professor Sir Michael Brady, that develops Transpara Breast AI for earlier detection of breast cancer in 2D (FFDM) and
  3D (DBT) mammography. Transpara is vendor-agnostic, integrates into PACS and reading workflows via medical-imaging (DICOM)
  pipelines rather than a public developer API, and is FDA cleared (K241831, K232096) and CE marked (0344). The product is
  used in over 200 clinics across 25+ countries and is backed by three randomized controlled trials (including MASAI in The
  Lancet). The company is backed by Insight Partners. This profile was enriched by the API Evangelist pipeline: no public
  developer/API surface exists, but real security-disclosure and compliance posture were captured.'
url: https://raw.githubusercontent.com/api-evangelist/screenpoint-medical/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- insight-partners
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-10-06'
tags:
- Company
- Healthcare
- Artificial Intelligence
- Medical Imaging
- Mammography
- Breast Cancer
- Radiology
- Machine Learning
tags_raw:
- Company
- Healthcare
- Artificial Intelligence
- Medical Imaging
- Mammography
- Breast Cancer
- Radiology
- Machine Learning
- Machine-Learning
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- url: https://screenpoint-medical.com/insights/rss.xml
  type: BlogRSS
- type: Website
  url: https://screenpoint-medical.com/
- type: Blog
  url: https://screenpoint-medical.com/insights
- type: PrivacyPolicy
  url: https://screenpoint-medical.com/privacy-policy-screenpoint-medical
- type: Security
  url: https://screenpoint-medical.com/security-reporting
- type: Compliance
  url: https://screenpoint-medical.com/security-reporting
- type: VulnerabilityDisclosure
  url: security/screenpoint-medical-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/screenpoint-medical-domain-security.yml
- type: Conformance
  url: conformance/screenpoint-medical-conformance.yml
x-enrichment:
  date: '2026-07-21'
  status: minimal
  artifacts_added: 3
  pass: local-v1

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get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

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