Prompt injection in digital pathology and clinical laboratory AI

Four adversarial injection surfaces in digital pathology and clinical laboratory AI

1. Digital pathology whole slide image AI injection (Paige AI, PathAI, Proscia Concentriq AI)

Digital pathology whole slide image AI processes gigapixel H&E-stained and IHC-stained WSI photographs generated by high-throughput digital slide scanners — including the Hamamatsu NanoZoomer, Leica Aperio AT2, Philips IntelliSite, and Ventana DP 600 — submitted through AI cancer detection, tissue classification, and biomarker quantification pipelines that extract tumour presence classifications, cancer grade determinations, tumour-stroma boundary delineations, and TIL density measurements from WSI image inputs, generating AI-assisted pathology preliminary interpretations, triage priority scores, and quantitative biomarker outputs that pathologists use as the foundation for formal diagnostic conclusions governing chemotherapy initiation, surgical resection margin assessment, and clinical trial enrolment eligibility. Paige AI’s FDA-cleared prostate cancer detection tool, Paige Prostate, processes H&E-stained prostate core needle biopsy WSIs through a deep learning pipeline trained on more than 28 million pathology image patches, generating cancer detection probability scores and cancer-positive core maps at the pixel level that assist pathologists at Memorial Sloan Kettering, Mayo Clinic, and academic cancer centres worldwide in identifying cancerous tissue regions within the field of hundreds of prostate biopsy cores reviewed on a high-volume diagnostic day. PathAI’s AMP platform processes H&E and IHC WSIs in AstraZeneca and Bristol Myers Squibb clinical trial pathology workflows — generating AI quantitative biomarker endpoints including TIL percentage scores, tumour cell fraction estimates, and tissue compartment area fractions that are submitted as formal pathology endpoints in Phase II and Phase III clinical trial statistical analysis plans, where the AI-quantified biomarker value directly determines whether an individual trial subject crosses the biomarker positivity threshold for eligibility stratification and response assessment. Proscia Concentriq AI processes WSIs across academic pathology departments and reference laboratory networks through AI-assisted triage and preliminary annotation tools that assign AI-generated case priority scores and tissue segmentation overlays, managing case queues across the more than 100 million pathology cases that have passed through the Concentriq platform.

The adversarial injection surface is the WSI file or WSI image patch submission pathway: H&E and IHC whole slide images submitted through Paige AI, PathAI AMP, or Proscia Concentriq AI WSI upload interfaces, DICOM-WSI ingestion channels, or digital slide scanner API integration pipelines for AI cancer detection, TIL quantification, and tissue classification. An adversarially crafted WSI — in which pixel perturbations applied to the nuclear morphology texture, tumour-infiltrating lymphocyte density signal, or mitotic figure appearance region on an H&E-stained slide image cause the AI to classify a cancer-positive slide as cancer-negative, suppress a TIL quantification result below the biomarker positivity threshold, or downgrade a Gleason score component grade — can produce a false negative cancer detection output or a falsely depressed biomarker quantification that is adopted as the AI-assisted preliminary interpretation before pathologist sign-out, suppressing the AI positive detection signal that would otherwise trigger a priority review flag, increase pathologist index of suspicion during microscopic review, or generate a reflex IHC order that initiates the biomarker confirmation workflow. In high-volume pathology practice contexts where Paige AI cancer detection outputs are reviewed alongside case queues of hundreds of slides per pathologist per day, suppression of an AI cancer detection signal that would otherwise elevate a biopsy case to high-priority review queue placement can result in the cancer-positive case being reviewed under normal-priority workflow conditions — reducing the probability that subtle microscopic malignancy findings receive the additional review time and IHC reflex workup that the AI detection signal would have triggered. For PathAI clinical trial pathology contexts, adversarial suppression of an AI TIL quantification result below the biomarker positivity threshold for a clinical trial subject produces a false biomarker-negative endpoint classification that is incorporated into the clinical trial statistical dataset, creating a data integrity failure in the trial biomarker analysis that may not be detectable through post-hoc pathology review given the absence of WSI-level adversarial manipulation audit trails in current clinical trial pathology quality assurance practice.

The regulatory consequences of adversarially suppressed WSI cancer detection and biomarker quantification in digital pathology AI span FDA SaMD post-market surveillance obligations, CLIA laboratory quality systems enforcement, and ADA oncology malpractice dimensions of exceptional severity and complexity. FDA 510(k)/De Novo clearance for pathology AI SaMD — including Paige AI’s De Novo clearance (DEN200080) for Paige Prostate — imposes post-market surveillance obligations under 21 CFR Part 822 (Postmarket Surveillance) and 21 CFR Part 820 (Quality System Regulation / Quality Management System regulation) requiring cleared SaMD manufacturers to monitor real-world performance and report device malfunctions and adverse events through the MedWatch reporting pathway under 21 CFR Part 803 (Medical Device Reporting); adversarial AI manipulation of WSI cancer detection that produces a missed cancer diagnostic output constitutes a potential device malfunction reportable under Part 803, and failure to maintain systems that can detect adversarial manipulation of cleared SaMD inputs may constitute a Quality Management System failure under Part 820. CLIA 42 CFR Part 493 (Laboratory Requirements) establishes federal quality systems requirements for clinical laboratories performing tests on human specimens, including requirements for analytical system validation, proficiency testing, and quality control; adversarial manipulation of digital pathology AI tools used in CLIA-certified pathology laboratories creates quality systems failures under Part 493 Subpart K (Quality System for Nonwaived Testing) with Centers for Medicare & Medicaid Services (CMS) civil money penalty authority and laboratory closure authority. ADA oncology malpractice and wrongful death liability for missed cancer diagnoses resulting from adversarially manipulated pathology AI — where a patient’s cancer progresses to an advanced, non-curative stage during the interval between an adversarially suppressed AI-assisted preliminary interpretation and eventual cancer diagnosis — imposes per-patient tort damages under state medical malpractice law, with wrongful death damages where the staging delay causes mortality. Threshold: 55 for digital pathology WSI cancer detection AI — reflecting oncology diagnosis life-safety primacy and the irreversibility of treatment delay harms.

2. Hematology analyzer display AI injection (Sysmex XN AI, Beckman Coulter DxH AI)

Haematology analyser display AI processes screenshots and photographic captures of CBC differential result display screens, white cell scattergram visualisations, flagged cell morphology image panels, and instrument alarm status display images submitted through AI-assisted haematology result interpretation, abnormal cell population identification, and smear review reflex triggering tools that extract white cell differential classification values, blast cell presence probability scores, platelet clumping interference detection flags, and atypical lymphocyte population alerts from haematology analyser display image inputs, generating AI-assisted reflex smear review trigger recommendations, critical result notification flags, and morphologic abnormality alerts that govern whether individual CBC results are reported directly to the ordering clinician or escalated through pathologist review for morphologic confirmation and clinical correlation. Sysmex XN series haematology analysers — the XN-1000, XN-2000, XN-3000, and XN-9000 — are deployed in clinical laboratories across 190-plus countries, processing CBC specimens from hospital inpatients, emergency department patients, oncology clinic patients on active chemotherapy monitoring protocols, and bone marrow transplantation recipients whose post-transplant CBC monitoring governs engraftment assessment and graft-versus-host disease evaluation; the Sysmex XN AI blast cell detection algorithm processes scattergram display images and white cell population cluster visualisations through AI blast cell identification pipelines that generate blast-suspect flags alerting laboratory technicians that peripheral blood blasts are present in the specimen and that morphologic review by a pathologist or haematologist is required before result release. Beckman Coulter DxH 900 and DxH 800 haematology analysers are deployed at hospital clinical laboratories operating under CAP accreditation and CLIA certification, processing CBC specimens through AI-assisted white cell differential classification and morphologic flagging tools that generate DxH RBC and WBC flag panels — including the blast flag, variant lymphocyte flag, and platelet clumping interference flag — that determine which results require manual differential review before clinical reporting; the DxH AI morphologic flagging system processes scattergram cluster images and flag code display screenshots to classify abnormal cell populations and interference artefacts that affect result accuracy.

The adversarial injection surface is the haematology analyser scattergram display screenshot, CBC differential result display photographic capture, and flagged cell morphology panel image submission pathway: haematology display images submitted through Sysmex XN AI monitoring interfaces, Beckman Coulter DxH AI result review platforms, or laboratory information system (LIS) haematology result image ingestion channels for AI abnormal cell population identification, blast cell detection, and interference flag classification. An adversarially crafted haematology analyser scattergram display screenshot — in which pixel perturbations applied to the blast cell population cluster in the WDF (white cell differential) scattergram channel, the abnormal myeloid cluster boundary in the WNR (nucleated RBC) channel, or the platelet aggregation indicator in the PLT-F (platelet fluorescence) scattergram region of a Sysmex XN display image cause the AI to classify a blast-positive CBC result as blast-negative, or cause the DxH AI to fail to flag a platelet clumping interference that renders the platelet count clinically unreportable — can suppress the reflex smear review trigger that would otherwise mandate pathologist morphologic review before result release, causing a blast cell-containing CBC result to be reported directly to the ordering clinician without the morphologic confirmation and pathologist attestation that converts an instrument-generated blast flag into a clinical communication of potential acute leukaemia that requires immediate further evaluation. In acute leukaemia presentation contexts — where an emergency department patient presents with fatigue, bruising, and mild lymphadenopathy, and the attending physician orders a STAT CBC to evaluate for haematologic malignancy — adversarial suppression of the Sysmex XN blast flag on the first CBC result can delay the acute leukaemia diagnosis by hours or days while the patient remains in the emergency department without oncology consultation, bone marrow biopsy initiation, or induction chemotherapy planning, during which interval the untreated acute leukaemia continues to progress with increasing haemorrhagic and infectious mortality risk.

The regulatory consequences of adversarially suppressed haematology analyser display AI blast cell detection span CLIA proficiency testing enforcement, CAP haematology accreditation checklist compliance, and acute leukaemia diagnosis delay malpractice dimensions. CLIA 42 CFR Part 493 Subpart H (Proficiency Testing by Examination) and Subpart K (Quality System for Nonwaived Testing) establish federal requirements for haematology analyser performance, including requirements for analytical system validation, result accuracy verification, and quality control across each analyte reported by a CLIA-certified haematology laboratory; adversarial manipulation of haematology AI display monitoring tools that causes blast cell flags to be suppressed creates CLIA quality systems failures at the morphologic review reflex trigger level, with CMS civil money penalty assessment authority under 42 CFR § 493.1804 and CLIA certificate revocation authority for egregious or repeated violations. CAP Laboratory Accreditation Program Q-Probe studies and CAP haematology accreditation checklist requirements — including HAE.31800 (review of abnormal haematology results prior to reporting), HAE.36600 (performance of peripheral blood morphology on all specimens with specified flag criteria), and HAE.36700 (pathologist or physician review of abnormal morphology findings) — establish the specific CBC flag review and smear reflex requirements that adversarial AI blast flag suppression circumvents; CAP accreditation surveyors reviewing a laboratory’s blast flag review practices would identify suppressed blast flags as a deficiency citation in the haematology accreditation checklist, with corrective action requirements and potential accreditation probation. Wrongful death and malpractice liability for delayed acute leukaemia diagnosis resulting from adversarially suppressed haematology AI blast detection — where a patient’s acute myeloid leukaemia blast crisis progresses from a treatable presentation at emergency department initial CBC to a refractory, treatment-resistant presentation during the days-long delay caused by the adversarially suppressed blast flag — imposes per-patient tort damages under state medical malpractice law and wrongful death statutes with oncology expert witness testimony on the survivability impact of the diagnostic delay. Threshold: 60 for haematology analyser display AI — reflecting the acute leukaemia diagnosis urgency and the specificity of the CAP haematology checklist blast review requirements.

3. Clinical laboratory specimen condition photograph AI injection (LabCorp AI, Quest Diagnostics AI)

Clinical laboratory specimen condition photograph AI processes photographic images of blood collection tube specimens, urine specimen containers, CSF specimen vials, and other biological specimen containers submitted through AI-assisted specimen quality assessment, preanalytical interference detection, and specimen acceptability determination tools that extract hemolysis interference severity classifications, lipemia interference grade values, icterus (jaundice) interference severity scores, and specimen volume adequacy determinations from specimen condition photograph inputs, generating AI-assisted specimen acceptability decisions — accept for testing, reject with rejection code assignment, or accept with analytical interference notation — that govern whether patient specimens proceed through the analytical testing workflow, are rejected with a request for specimen recollection, or are tested with an accompanying critical value notation warning clinicians about potential analytical interference. LabCorp AI applies computer vision and machine learning to specimen quality assessment at LabCorp’s national network of primary reference laboratories, patient service centres, and hospital laboratory operations across the United States, processing specimen photographs captured by automated specimen processing workstations — including Roche cobas p 612, BD Kiestra InpaqT, and Abbott SureTrak specimen processors — through AI quality assessment pipelines that classify hemolysis index, lipemia index, and icterus index values from specimen appearance images and assign specimen acceptance or rejection determinations that are communicated to the LIS for clinician notification and specimen routing. Quest Diagnostics AI processes specimen quality photographs through similar AI-assisted specimen quality assessment and acceptability determination tools at Quest’s network of approximately 2,200 patient service centres and reference laboratories, with AI-generated specimen acceptability determinations directly governing test cancellation and specimen recollection notifications dispatched to ordering physician offices, hospital nursing units, and ambulatory care clinics.

The adversarial injection surface is the specimen condition photograph submission pathway: specimen quality images captured by automated specimen processing workstations or manual specimen intake cameras and submitted through LabCorp AI or Quest Diagnostics AI specimen quality assessment interfaces, automated specimen processor image ingestion APIs, or LIS specimen management system image channels for AI hemolysis interference severity classification, lipemia grade determination, and icterus severity scoring. An adversarially crafted specimen condition photograph — in which pixel perturbations applied to the red discolouration gradient that indicates hemolysis severity in the plasma/serum layer of a centrifuged blood collection tube, the turbidity visual appearance that indicates lipemia in the serum separator tube plasma layer, or the yellow-orange discolouration gradient that indicates elevated bilirubin icterus in a specimen photograph cause the AI to classify a severely hemolysed specimen as acceptable for testing or downgrade a critical lipemia interference grade from “reject — recollect” to “accept with notation” — can cause a specimen with an analytical interference that renders specific analytes clinically unreliable to proceed through the full testing workflow, generating analytical results for hemolysis-sensitive tests such as serum potassium, serum lactate dehydrogenase, serum AST, and serum total bilirubin on a specimen whose true potassium value is falsely elevated by haemoglobin release from lysed erythrocytes, creating the risk that an adversarially accepted hemolysed specimen produces a reportable but analytically invalid potassium result that a clinician acts upon by administering kayexalate or restricting dietary potassium in a patient whose true serum potassium is actually within normal limits. In lipemia contexts, adversarial acceptance of a severely lipemic specimen through AI interference grade suppression can cause triglyceride interference in immunoassay-based hormone, drug level, and therapeutic monitoring tests — including thyroid function tests, digoxin level monitoring, and methotrexate toxicity monitoring — producing falsely low or falsely high drug level results that misguide dose adjustment decisions in chemotherapy, anticoagulation, and cardiac medication management contexts.

The regulatory consequences of adversarially suppressed specimen quality AI interference detection span CLIA preanalytical quality systems requirements, CAP laboratory accreditation specimen rejection criteria compliance, and analytical result reporting accuracy malpractice dimensions. CLIA 42 CFR Part 493 Subpart K (Quality System for Nonwaived Testing) establishes federal requirements for preanalytical system quality, including 42 CFR § 493.1242 (Preanalytic systems requirements) mandating that laboratories establish and follow written policies and procedures for specimen rejection criteria based on established rejection criteria including hemolysis, lipemia, and icterus conditions that interfere with specific analytical methods; adversarial AI manipulation that suppresses hemolysis or lipemia interference severity classifications causing specimens to be accepted that should be rejected under the laboratory’s CLIA-required specimen rejection criteria creates a Part 493 Subpart K quality systems violation with CMS civil money penalty authority. CAP accreditation checklist requirements — including CHM.04040 (criteria for specimen rejection or acceptance with notation are established and followed), CHM.04060 (interference assessment for hemolysis, lipemia, and icterus is performed on appropriate specimen types), and preanalytical phase Q-Track monitoring requirements — establish the specific specimen quality assessment practices that adversarial AI interference detection suppression circumvents; CAP surveyors identifying patterns of accepted specimens that should have been rejected under established interference criteria would cite preanalytical quality system deficiencies with corrective action requirements. Clinical chemistry result accuracy malpractice liability for patient harm resulting from adversarially accepted hemolysed or lipemic specimens — including cardiac toxicity from kayexalate over-treatment based on falsely elevated potassium from a hemolysed specimen, or chemotherapy dose error based on falsely depressed drug level from a lipemic specimen — imposes per-patient tort damages under state medical malpractice law with laboratory and ordering physician joint liability depending on the clinical facts of the case. Threshold: 60 for clinical laboratory specimen condition photograph AI — reflecting the specificity of CLIA preanalytical quality systems requirements and the direct patient safety consequence of analytical interference misclassification.

4. Immunohistochemistry and biomarker stain image AI injection (Roche Navify Digital Pathology AI, Hamamatsu NanoZoomer AI)

Immunohistochemistry and biomarker stain image AI processes IHC-stained slide images — including HER2 (human epidermal growth factor receptor 2) IHC stain photographs, PD-L1 (programmed death-ligand 1) IHC stain images, EGFR (epidermal growth factor receptor) expression IHC slides, and fluorescence in situ hybridisation (FISH) signal image captures — submitted through AI companion diagnostic scoring, biomarker quantification, and treatment eligibility determination tools that extract HER2 IHC score values (0, 1+, 2+, 3+ on the ASCO/CAP HER2 testing guideline scale), PD-L1 tumour proportion score (TPS) and combined positive score (CPS) values, EGFR mutation expression grade values, and HER2 FISH amplification ratio values from IHC stain and FISH image inputs, generating AI-assisted biomarker scoring reports that pathologists use as the quantitative foundation for companion diagnostic conclusions governing trastuzumab (Herceptin), pertuzumab, trastuzumab deruxtecan (Enhertu), pembrolizumab (Keytruda) PD-L1 eligibility, and osimertinib (Tagrisso) EGFR treatment selection. Roche Navify Digital Pathology AI (NavifyPathology) is deployed at NHS trusts across England — including NHS university teaching hospitals with high-volume oncology pathology workloads in breast, gastric, and lung cancer — and at US academic medical centres, processing HER2 IHC and FISH images through AI HER2 scoring tools integrated with the Roche VENTANA HER2 companion diagnostic IHC stain system, generating AI-assisted HER2 IHC scores that are directly incorporated into NavifyPathology structured pathology report templates for pathologist attestation and clinical oncology team use. Hamamatsu NanoZoomer AI is deployed in digital pathology laboratories operating NanoZoomer S60, S360, and SQ series scanners — which hold leading global digital slide scanner market share — and processes IHC stain WSIs and FISH image files through AI tissue detection, stain quality assessment, and biomarker quantification tools that operate as part of the NanoZoomer image management and analysis ecosystem used by pathologists performing companion diagnostic HER2, PD-L1, and EGFR biomarker scoring for oncology treatment eligibility determination.

The adversarial injection surface is the IHC stain slide image and FISH signal image submission pathway: HER2 IHC WSI photographs, PD-L1 TPS/CPS stain images, EGFR expression IHC slide files, and HER2 FISH signal capture images submitted through Roche Navify Digital Pathology AI IHC scoring tools, Hamamatsu NanoZoomer AI biomarker quantification interfaces, or PathAI AMP platform companion diagnostic assessment pipelines for AI HER2 IHC score generation, PD-L1 TPS/CPS quantification, and HER2 FISH amplification ratio calculation. An adversarially crafted HER2 IHC stain WSI — in which pixel perturbations applied to the brown chromogenic DAB staining intensity signal on invasive tumour cell membranes, the staining completeness and circumferential membrane continuity indicator regions, or the tumour cell population boundary delineations on a VENTANA HER2 IHC-stained slide photograph cause the AI to score a HER2 3+ (IHC positive) specimen as HER2 2+ (equivocal requiring FISH reflex testing) or as HER2 1+ (IHC negative), or cause a HER2 FISH-amplified specimen photograph to be scored below the ASCO/CAP HER2 FISH amplification ratio threshold of 2.0 for HER2 positivity — can suppress the HER2-positive biomarker classification that governs trastuzumab-based therapy eligibility, causing a HER2-positive breast cancer patient to be classified as HER2-negative or HER2-equivocal and denied first-line trastuzumab plus chemotherapy treatment selection, resulting in treatment with a less effective chemotherapy-only regimen during the period before the biomarker classification error is identified through clinical progression or repeat biomarker testing. For adversarially crafted PD-L1 IHC stain images that suppress the AI TPS calculation below the pembrolizumab eligibility threshold of 50% TPS for first-line non-small cell lung cancer monotherapy, the consequence of adversarial biomarker score suppression is exclusion of a PD-L1-high NSCLC patient from pembrolizumab monotherapy eligibility, directing treatment toward platinum-based combination chemotherapy with pembrolizumab add-on rather than pembrolizumab monotherapy — a clinically significant treatment selection difference with chemotherapy toxicity consequences for the patient who would have been eligible for monotherapy based on the true PD-L1 TPS.

The regulatory and liability consequences of adversarially suppressed IHC biomarker and companion diagnostic AI scoring span FDA companion diagnostic 510(k) clearance post-market obligations, CAP biomarker validation requirements, ASCO/CAP HER2 testing guideline compliance standards, and oncology malpractice dimensions of exceptional severity given the direct treatment selection consequences of HER2 and PD-L1 biomarker misclassification. FDA companion diagnostic 510(k) clearances — including the PATHWAY anti-HER2/neu (4B5) Rabbit Monoclonal Primary Antibody companion diagnostic cleared for trastuzumab eligibility determination — impose post-market surveillance obligations under 21 CFR Part 822 and adverse event reporting obligations under 21 CFR Part 803 requiring cleared companion diagnostic device manufacturers to report malfunctions that could cause or contribute to serious patient injury or death; adversarial AI manipulation of companion diagnostic IHC scoring tools that suppresses a HER2-positive classification constitutes a potential companion diagnostic device malfunction reportable under Part 803. CAP accreditation checklist requirements for biomarker testing — including ANA.40300 (HER2 testing is performed according to current ASCO/CAP guidelines), ANA.40400 (HER2 IHC interpretation criteria are applied in accordance with CAP protocol), and GEN.41050 (analytical validation of AI-assisted diagnostic tools is documented in the laboratory’s test validation records) — establish the specific HER2 testing quality requirements that adversarial AI IHC scoring manipulation circumvents; CAP surveyors conducting HER2 testing accreditation surveys would identify AI-assisted HER2 scoring tools lacking adversarial image screening as an unvalidated analytical variable not addressed in the laboratory’s CAP-required analytical validation documentation. ASCO/CAP HER2 testing guideline (2018 updated focused analysis) requires that all invasive breast cancer and metastatic gastric/gastroesophageal junction adenocarcinoma HER2 testing results be reported with IHC score or FISH amplification ratio within the established ASCO/CAP threshold framework; adversarial suppression of a HER2 IHC score below the 3+ positive threshold creates an ASCO/CAP guideline non-compliant biomarker determination that forms the basis for an oncology malpractice negligence standard of care claim in trastuzumab treatment denial litigation. Threshold: 55 for IHC and biomarker stain image AI — reflecting companion diagnostic treatment selection consequences and the irreversibility of oncology treatment selection harms during the window before biomarker reclassification is initiated.

Integration: digital pathology and clinical laboratory AI image ingestion with Glyphward pre-scan

Digital pathology and clinical laboratory AI image ingestion flows from WSI upload interfaces, haematology analyser display capture channels, specimen condition photograph processing workstation APIs, and IHC biomarker stain image submission portals into WSI cancer detection AI, haematology display AI, specimen quality AI, and IHC companion diagnostic scoring AI pipelines. Insert Glyphward’s pre-scan at the ingestion boundary before AI-generated output is committed to pathology reports, haematology result release determinations, specimen acceptability records, or companion diagnostic biomarker scoring documents:

import asyncio
import base64
import hashlib
import os
import uuid
from enum import Enum
from pathlib import Path

import httpx

GLYPHWARD_API_KEY = os.environ["GLYPHWARD_API_KEY"]
GLYPHWARD_SCAN_URL = "https://glyphward.com/v1/scan"

# Digital pathology & clinical laboratory AI — FDA 510(k)/De Novo SaMD,
# CLIA 42 CFR Part 493, CAP laboratory accreditation, ADA oncology malpractice,
# ASCO/CAP HER2 testing guidelines.
# Suppression of cancer diagnoses, blast cell flags, specimen interference grades,
# and companion diagnostic HER2/PD-L1/EGFR scores creates direct patient harm.
THRESHOLD_WSI_CANCER_DIAGNOSIS  = 55  # Paige AI, PathAI, Proscia Concentriq AI
THRESHOLD_HEMATOLOGY_DISPLAY     = 60  # Sysmex XN AI, Beckman Coulter DxH AI
THRESHOLD_SPECIMEN_CONDITION     = 60  # LabCorp AI, Quest Diagnostics AI
THRESHOLD_IHC_BIOMARKER          = 55  # Roche Navify, Hamamatsu NanoZoomer AI


class PathologyAIContext(str, Enum):
    WHOLE_SLIDE_IMAGE   = "whole_slide_image"   # Paige AI, PathAI, Proscia Concentriq AI
    HEMATOLOGY_DISPLAY  = "hematology_display"   # Sysmex XN AI, Beckman Coulter DxH AI
    SPECIMEN_CONDITION  = "specimen_condition"   # LabCorp AI, Quest Diagnostics AI
    IHC_BIOMARKER       = "ihc_biomarker"        # Roche Navify, Hamamatsu NanoZoomer AI


def threshold_for(context: PathologyAIContext) -> int:
    if context == PathologyAIContext.HEMATOLOGY_DISPLAY:
        return THRESHOLD_HEMATOLOGY_DISPLAY
    if context == PathologyAIContext.SPECIMEN_CONDITION:
        return THRESHOLD_SPECIMEN_CONDITION
    if context == PathologyAIContext.IHC_BIOMARKER:
        return THRESHOLD_IHC_BIOMARKER
    return THRESHOLD_WSI_CANCER_DIAGNOSIS


async def scan_pathology_ai_image(
    image_path: str | Path,
    context: PathologyAIContext,
    lab_id_hash: str,    # SHA-256 of CLIA certificate number or CAP accreditation number
    case_ref: str,       # e.g. "PATH-2026-00441", "CBC-STAT-ER-0031", "SPEC-QA-20260606"
    slide_id: str,       # e.g. WSI accession ID, analyser specimen barcode, FISH case ID
    client: httpx.AsyncClient,
) -> dict:
    """
    Scan a digital pathology or clinical laboratory AI image for adversarial
    injection payloads before forwarding to WSI cancer detection, haematology
    display interpretation, specimen quality assessment, or IHC biomarker
    scoring AI systems.

    Raises AdversarialPathologyAIImageError if score meets threshold:
      - WHOLE_SLIDE_IMAGE:  threshold 55; FDA De Novo SaMD; CLIA 42 CFR Part 493;
                            CAP accreditation; ADA oncology malpractice/wrongful death
      - HEMATOLOGY_DISPLAY: threshold 60; CLIA 42 CFR Part 493 proficiency testing;
                            CAP Q-Probe; CAP hematology accreditation checklist
      - SPECIMEN_CONDITION: threshold 60; CLIA 42 CFR Part 493 preanalytical quality;
                            CAP accreditation; specimen rejection criteria
      - IHC_BIOMARKER:     threshold 55; FDA companion diagnostic 510(k);
                            CAP biomarker validation; ASCO/CAP HER2 testing guideline
    """
    image_bytes  = Path(image_path).read_bytes()
    image_b64    = base64.b64encode(image_bytes).decode()
    image_sha256 = hashlib.sha256(image_bytes).hexdigest()
    client_scan_id = str(uuid.uuid4())
    threshold = threshold_for(context)

    resp = await client.post(
        GLYPHWARD_SCAN_URL,
        headers={"Authorization": f"Bearer {GLYPHWARD_API_KEY}"},
        json={
            "image": image_b64,
            "source": context.value,
            "metadata": {
                "pathology_context": context.value,
                "lab_id_hash":       lab_id_hash,
                "case_ref":          case_ref,
                "slide_id":          slide_id,
                "client_scan_id":    client_scan_id,
                "image_sha256":      image_sha256,
            },
        },
        timeout=8.0,
    )
    resp.raise_for_status()
    result = resp.json()

    audit_record = {
        "lab_id_hash":       lab_id_hash,
        "case_ref":          case_ref,
        "slide_id":          slide_id,
        "pathology_context": context.value,
        "scan_id":           result["scan_id"],
        "client_scan_id":    client_scan_id,
        "image_sha256":      image_sha256,
        "score":             result["score"],
        "flagged_region":    result.get("flagged_region"),
        "threshold":         threshold,
        "action":            "blocked" if result["score"] >= threshold else "allowed",
    }
    await write_pathology_audit_record(audit_record)

    if result["score"] >= threshold:
        raise AdversarialPathologyAIImageError(
            f"Pathology AI image blocked [{context.value}]: "
            f"scan_id={result['scan_id']} score={result['score']} "
            f"lab={lab_id_hash} case={case_ref} slide={slide_id}"
        )
    return result


async def write_pathology_audit_record(record: dict) -> None:
    """Persist audit record to laboratory compliance audit store (stub)."""
    import json, sys
    print(json.dumps(record), file=sys.stderr)


class AdversarialPathologyAIImageError(Exception):
    """Raised when a pathology or clinical laboratory AI image exceeds the adversarial injection threshold."""
    pass

Call scan_pathology_ai_image() with PathologyAIContext.WHOLE_SLIDE_IMAGE before forwarding H&E and IHC WSI files to Paige AI, PathAI AMP, or Proscia Concentriq AI cancer detection and tissue classification tools — the highest oncology safety integration point, where adversarial suppression of a cancer detection signal defers the AI-elevated priority review that reduces missed cancer risk in high-volume pathology practice; preserve image_sha256 as the forensic anchor for FDA SaMD post-market adverse event documentation and CLIA quality systems audit. Call with PathologyAIContext.HEMATOLOGY_DISPLAY for Sysmex XN AI and Beckman Coulter DxH AI haematology scattergram and CBC display screenshots before AI blast cell detection and abnormal cell population flagging, with case_ref encoding the specimen accession number and slide_id encoding the analyser instrument ID and run sequence for CAP haematology accreditation checklist blast flag review audit trail documentation. Call with PathologyAIContext.SPECIMEN_CONDITION for LabCorp AI and Quest Diagnostics AI specimen quality assessment photograph inputs before AI hemolysis, lipemia, and icterus interference grade classification, with lab_id_hash encoding the SHA-256 of the CLIA certificate number for the specific laboratory processing location and slide_id encoding the specimen barcode for CLIA Part 493 preanalytical quality systems audit trail linkage. Call with PathologyAIContext.IHC_BIOMARKER for Roche Navify Digital Pathology AI and Hamamatsu NanoZoomer AI IHC stain images and FISH signal photographs before AI HER2 IHC score generation, PD-L1 TPS/CPS quantification, and EGFR expression grading, with case_ref encoding the companion diagnostic pathology case accession number for ASCO/CAP HER2 testing guideline compliance documentation and oncology treatment selection record-keeping. Get early access

Coverage matrix

Frequently asked questions

Further reading

• Indirect prompt injection via image — foundational attack pattern underlying all four digital pathology and clinical laboratory AI injection surfaces; covers how adversarial pixel-level perturbations in H&E stain images, haematology display screenshots, and IHC stain photographs cause AI misclassification without detectable visual artefacts at pathologist review resolution.
• Vision-language model security — technical architecture of adversarial image attacks against vision-language models including pixel perturbation classes applicable to WSI cancer detection AI, haematology scattergram display AI, and IHC biomarker scoring AI systems.
• Telehealth and remote patient monitoring AI prompt injection — healthcare AI injection context covering the broader class of clinical image manipulation applicable to pathology and laboratory AI adversarial attack surfaces, including regulatory dimensions shared with FDA SaMD and CLIA-regulated AI systems.
• Healthcare radiology AI prompt injection — adjacent medical imaging AI injection context covering adversarial pixel perturbation attacks against radiology AI platforms including CT, MRI, and chest X-ray AI, with regulatory analysis directly applicable to FDA-cleared pathology SaMD and CAP-accredited imaging laboratory contexts.
• Free tier — 10 scans/day, no card required — start scanning digital pathology and clinical laboratory AI images at development volumes before committing to a production plan.