Part 14/Chapter 72/5-min read

AI, Telemedicine, Information Quality, Global Vascular Surgery, and Future Directions

AI in vascular surgery is most useful when it supports imaging, measurement, triage, or workflow decisions under clinician oversight. Deployment requires reporting quality, governance, privacy protection, equity monitoring, and lifecycle surveillance. Telemedicine can improve access when escalation back to in-person care is explicit, while patient-facing information needs clinician curation. Global vascular care should be planned as tiered capacity across prevention, diagnosis, treatment, referral, follow-up, supplies, and training. Future-facing AI reports should guide priorities without being treated as proof of improved outcomes.

Edited by Tal M. Hörer MD, PhD · David T. McGreevy MD, PhD·Reviewed ·6 sections · 16 references

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Artificial intelligence readiness and reporting

Mature vascular artificial intelligence applications assist with image classification, segmentation, structured measurement, case identification, triage, or risk stratification, leaving the final clinical action to a clinician . Autonomous operative capabilities and unsupervised bedside decision-making remain experimental . Vascular artificial intelligence tools function as decision support to narrow attention or standardise measurement, and they do not remove clinical accountability.

Model deployment requires three distinct layers of validation:

  • Technical validity: the model performs accurately on data resembling the intended local patients, scanners, and clinical records.
  • Clinical validity: the output aligns with a genuine decision point, such as urgent assessment, repeat imaging, or multidisciplinary discussion.
  • Workflow validity: the result arrives at the correct time, to the correct person, in an actionable form without causing silent delay, duplicated work, or alarm fatigue.

Standardised reporting tools govern clinical artificial intelligence evaluation. DECIDE-AI structures early live clinical evaluation, CONSORT-AI extends randomised-trial reporting, and SPIRIT-AI guides protocol reporting .

Clinical Artificial Intelligence Reporting Requirements
DECIDE-AI, CONSORT-AI, and SPIRIT-AI
Scope
Early live evaluation, trials, and protocols
Required elements
Intended use, model version, inputs, human interaction, comparator, workflow integration, and failure modes
Citation
WHO governance and FDA software guidance
Scope
Healthcare artificial intelligence tools and programs
Required elements
Transparency, accountability, equity, privacy, human oversight, lifecycle monitoring, and regulatory jurisdiction
Citation

Governance and lifecycle monitoring

Governance determines whether an accurate model remains safe in clinical practice. The World Health Organization (WHO) and the United States Food and Drug Administration (FDA) frameworks require defined intended use, accountability, equity, privacy, human oversight, and lifecycle monitoring .

The intended use statement defines the patient population, input source, decision point, user, and permitted action. A model trained for opportunistic aneurysm screening is not automatically valid for operative planning. Governance frameworks mandate specific operational controls before a tool is used in patient care.

Risk FactorsArtificial intelligence deployment and governance parameters
  • Pre-deployment
    Requirement
    Equity and privacy assessment
    Clinical implementation
    Verify performance across age, sex, ethnicity, socioeconomic context, and imaging equipment; secure sensitive imaging and operative records
  • Live deployment
    Requirement
    Defined accountability
    Clinical implementation
    Explicit assignment of who reviews outputs, who holds override authority, and who manages pathway failures
  • Post-deployment
    Requirement
    Lifecycle monitoring
    Clinical implementation
    Defined schedule to monitor performance drift caused by changing referral patterns, hardware, or population shifts, including triggers for model withdrawal
Sources

Telemedicine workflows and escalation

Telemedicine pathways are safe only when integrated with defined clinical escalation routes . Remote care is established for structured postoperative wound checks with images, selected endovascular follow-up, medication management, surveillance-result discussion, and stable symptom triage. The pathway defines which symptoms are suitable for remote assessment and which findings mandate immediate direct examination.

Absolute triggers for hands-on evaluation include:

  • Enlarging wounds or new tissue loss.
  • New rest pain or a cool extremity.
  • Neurological symptoms following carotid intervention.
  • Dialysis access dysfunction.
  • Fever following graft placement.

Telemedicine programs monitor equity to ensure remote pathways do not exclude patients with limited digital literacy, poor connectivity, sensory impairment, insecure housing, or language barriers. Routine quality tracking includes missed escalations, delayed imaging, and unplanned admissions .

Patient-facing information quality

Patient-facing vascular information on the internet has documented limitations in readability, usability, completeness, and reliability . Uncurated online resources frequently omit conservative management options, exaggerate procedural benefits, and minimise complication risks, which distorts shared decision-making.

Clinical services curate information pathways by correcting inaccurate risk statements and providing validated materials matched to the patient's specific anatomy, comorbidity, and treatment goals. For conditions such as carotid stenosis, aneurysm repair, and limb revascularization, curated information must explicitly state the diagnosis, natural history, medical therapy, procedural options, surveillance requirements, warning symptoms, and the consequences of no intervention.

Global vascular capacity planning

Essential vascular care in lower-resource settings relies on tiered capacity development rather than isolated technological procurement . Purchasing advanced imaging or endovascular devices fails to improve outcomes if transport, medical supply chains, or complication-management systems are absent.

Sequential tiers of vascular capacity development:

  • Prevention and risk reduction: smoking cessation, hypertension and diabetes control, antiplatelet therapy, and statin access.
  • Diagnostic assessment: clinical examination and reliable duplex ultrasound, with cross-sectional imaging where feasible.
  • Interventional capability: reliable medical therapy and basic operative capability, progressing to endovascular services.
  • Infrastructure: supply chains for drugs, grafts, and devices, accompanied by referral systems, transport networks, and structured follow-up.
  • Workforce: training, recruitment, and retention of vascular surgeons and allied specialists to sustain the tiers above.

System planning begins by mapping patient flow and local disease burden to identify the missing foundational tier, establishing basic referral links and stable medical therapies before promising advanced intervention access .

Areas of controversy

The clinical utility of autonomous operative artificial intelligence and computer-vision scene understanding is unsettled; current literature largely represents horizon scanning rather than proven outcome benefits, requiring prospective external validation before altering practice .

The balance between telemedicine efficiency and the exacerbation of healthcare disparities remains contested, as digital pathways may systematically disadvantage vulnerable populations if strict equity monitoring is absent .

Jurisdictional variations in classifying clinical artificial intelligence software as a medical device create uneven regulatory thresholds for deployment, oversight, and lifecycle monitoring, complicating the adoption of global standards .

References

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    PubMed-indexed articleReview2023

    Mature artificial intelligence and machine learning-enabled medical tools that may impact vascular surgical care. 2023. doi:10.1053/j.semvascsurg.2023.06.001.

    PubMedDOI
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    Machine learning in vascular surgery: a systematic review and critical appraisal. 2022.
    PubMed-indexed articleMeta-analysis / systematic review2022

    Machine learning in vascular surgery: a systematic review and critical appraisal. 2022. doi:10.1038/s41746-021-00552-y.

    PubMedDOI
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    Computer vision applications in vascular surgery: a systematic review and critical appraisal. 2026.
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    Computer vision applications in vascular surgery: a systematic review and critical appraisal. 2026. doi:10.1038/s41746-026-02427-6.

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    The Operative Role of Artificial Intelligence in Vascular Surgery: A Systematic Review of Literature. 2025. doi:10.7759/cureus.95515.

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