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

72.1Where vascular AI is mature and where it is still developing

The initial clinical question about vascular artificial intelligence is whether the model is being asked to solve a bounded problem that belongs in the care pathway. The most mature applications are generally those that assist with image classification, segmentation, structured measurement, case identification, triage, or risk stratification, where the output is checked by a clinician and connected to a predefined next step. Syntheses of vascular AI and machine-learning tools describe the strongest near-term substrate in imaging-heavy and workflow-linked tasks, while autonomous treatment choice, operative judgment, and unsupervised bedside decision-making remain much less established Machine learning vascular 2022 · Mature artificial intelligence 2023. Recent vascular AI discussion has also moved from generic enthusiasm toward more specific questions: which computer-vision tasks are externally validated, whether operative-assistance tools change decisions or merely document workflow, and how future-facing AI proposals should be evaluated before they enter routine vascular care Computer vision applications 2026 · Operative Role Artificial 2025 · Artificial intelligence magic 2024. This distinction matters at the workstation and in clinic: a tool that measures aneurysm diameter, flags a surveillance scan for clinician assessment, or prioritises an abnormal duplex report is different from a tool that implies a revascularization decision.

Readiness should therefore be judged in layers. The first layer is technical validity: the model must perform adequately on data that resemble the patients, scanners, imaging protocols, and clinical records in the intended setting. The second is clinical validity: the output must correspond to a decision that clinicians actually need, such as whether a patient requires urgent assessment, repeat imaging, referral, optimization of medical therapy, or discussion at a multidisciplinary meeting. The third is workflow validity: the result must arrive at the right time, to the right person, in a form that can be acted on without creating silent delay, duplicated work, or excessive alarm fatigue. A high area-under-the-curve value is helpful only if these layers are aligned; without them, the tool may be accurate in a study and still unhelpful in a vascular service.

The reporting standards used to judge clinical AI work provide a useful discipline for vascular readers. DECIDE-AI addresses early live clinical evaluation of AI systems, CONSORT-AI extends randomized-trial reporting for AI interventions, and SPIRIT-AI extends protocol reporting for AI trials DECIDE-AI reporting guideline. 2022 · CONSORT 2020 · SPIRIT-AI extension. 2020. A vascular AI report should make the intended use clear, identify the model version, describe the input data and pre-processing, explain how the clinician interacts with the output, name the comparator, state how the tool is inserted into workflow, and describe foreseeable failure modes. These standards do not prove that a tool improves limb salvage, stroke prevention, aneurysm surveillance, or operating-room performance, but they help the reader decide whether the study can be interpreted at all.

The safest posture for trainees and services is to treat AI as decision support until stronger evidence shows otherwise. In aneurysm surveillance, carotid imaging, peripheral arterial disease triage, venous imaging, or access planning, the clinician remains responsible for reconciling the model output with symptoms, anatomy, comorbidity, prior imaging, procedural feasibility, and patient goals. The useful AI tool narrows attention or standardises measurement; it does not remove clinical accountability.

72.2Governance sits alongside accuracy, not after it

A vascular AI tool requires more than initial validation to remain safe. The clinical safety case includes the model, the data, the workflow, the people who act on the output, and the plan for what happens when performance changes. WHO ethics and regulatory guidance for health AI and the FDA approach to artificial-intelligence and machine-learning software as a medical device emphasise recurring requirements: transparency about intended use and limits, accountability for clinical action, attention to equity and bias, privacy and cybersecurity protection, human oversight, lifecycle monitoring, and jurisdiction-specific regulatory evaluation when software functions as a medical device WHO 2023 · WHO 2021 · FDA 2021. These requirements determine whether the output can be trusted in a real vascular pathway.

The intended use statement is the reference point. A model designed to identify abdominal aortic aneurysms on opportunistic imaging is not automatically suitable for operative planning, and a risk model trained to prioritise follow-up appointments is not automatically suitable for withholding in-person assessment. The intended patient population, input source, decision point, user, and permitted action should be explicit before a service uses the tool. This is particularly important in vascular surgery because disease prevalence, imaging quality, case mix, and procedural thresholds vary between screening programs, emergency departments, outpatient clinics, hybrid theatres, and regional referral networks.

Accountability must also be assigned before deployment. The service should know who sees the output, who confirms it, who can override it, who contacts the patient, and who is responsible if the tool is unavailable or gives a result that conflicts with clinical judgment. Human oversight is meaningful only when it is operationalised: the clinician needs enough information to understand the output, enough time to assess it, and enough authority to stop or modify the pathway. Reporting standards such as DECIDE-AI, CONSORT-AI, and SPIRIT-AI support this governance work because they require clearer description of the model, the human–AI interaction, the comparator, and the planned evaluation context DECIDE-AI reporting guideline. 2022 · CONSORT 2020 · SPIRIT-AI extension. 2020.

Equity and privacy are central in vascular care. If an AI tool performs well in a population unlike the local service population, it may widen existing differences in access, surveillance, or treatment. A governance assessment should therefore ask whether performance has been examined across age, sex, ethnicity, socioeconomic context, geography, imaging equipment, and disease severity when those variables are relevant to the pathway. Privacy and cybersecurity questions are equally clinical: vascular imaging, longitudinal records, operative details, and comorbidity profiles are sensitive data, and a breach or unsafe integration can damage patients even when the algorithm itself is accurate WHO 2023 · WHO 2021 · FDA 2021.

Lifecycle monitoring is the part most easily missed. Model performance can drift when scanners change, referral patterns shift, coding practices evolve, or the patient population becomes different from the training population. A vascular service should define what will be monitored, how often performance will be assessed, which safety signals trigger investigation, how model updates are controlled, and how the tool will be withdrawn if it begins to behave unpredictably. Accuracy is the beginning of the deployment conversation, not the end.

72.3Telemedicine and patient-facing information quality

Telemedicine in vascular surgery is safest when the pathway begins with the clinical task rather than the communication platform. Some tasks fit remote care well: postoperative wound checks with images, structured assessment after selected endovascular procedures, medication and risk-factor follow-up, surveillance-result discussion, triage of stable symptoms, and pre-visit preparation for patients who already have imaging. Systematic syntheses and vascular digital-health summaries describe telemedicine and eHealth use across monitoring, diagnosis, follow-up, and communication, but they also describe a heterogeneous evidence base in which patient selection, digital access, local workflow, and escalation arrangements strongly influence value Telemedicine Vascular Surgery 2023 · Systematic Review Telemedicine 2022 · Telemedicine Digital Health 2022. The question for the vascular service is whether the pathway preserves clinical safety when the remote encounter reveals a problem, rather than whether the appointment can technically be performed by video, telephone, or image upload.

A useful remote pathway has explicit boundaries. It defines which symptoms are suitable for remote assessment, which patients need direct examination, which photographs or measurements are required, and which findings trigger duplex, computed-tomography angiography, admission, urgent clinic assessment, or emergency care. This is particularly important for vascular patients because deterioration may be visual, physiological, or symptom-based: an enlarging wound, new rest pain, a cool foot, neurological symptoms after carotid intervention, access dysfunction, or fever after graft surgery cannot be safely managed by reassurance if the pathway lacks a route back to hands-on evaluation. Telemedicine can reduce travel burden and improve access in selected patients, but digital contact should not be counted as adequate care when it substitutes for examination, imaging, or intervention that the patient actually needs Telemedicine Vascular Surgery 2023 · Systematic Review Telemedicine 2022 · Telemedicine Digital Health 2022.

The equity test is just as important as the efficiency test. Remote care may help rural patients, patients with limited mobility, and patients for whom travel is costly or risky. It may also exclude patients with poor connectivity, limited digital literacy, sensory impairment, language barriers, cognitive impairment, insecure housing, or lack of a private place to speak. A vascular telemedicine program should therefore track not only appointment completion but also missed escalation, delayed imaging, unplanned admissions, complications, and whether particular groups are being lost to follow-up. The digital channel should shorten the distance to appropriate care, not create a second doorway that is easier for some patients and unsafe for others.

Patient-facing information is the other digital surface of vascular practice. Patients commonly arrive with search results, commercial webpages, hospital leaflets, social-media claims, or family advice already shaping their expectations. Direct assessment of vascular surgery websites has found problems with readability, usability, completeness, and reliability, supporting the need for clinician-curated information pathways rather than passive reliance on what patients find online Quality vascular surgery 2012. Clinicians should ask what the patient has read, correct inaccurate risk statements, explain which options apply to their anatomy and comorbidity, and provide written or digital materials that the team is prepared to stand behind.

Good information curation improves shared decision-making. For carotid stenosis, aneurysm repair, limb revascularization, venous disease, dialysis access, or wound care, the patient needs to understand the diagnosis, natural history, medical therapy, procedural options, surveillance, warning symptoms, and the consequences of doing nothing. Online information that omits conservative care, exaggerates procedural benefit, minimises complications, or hides uncertainty can distort consent. The vascular clinician’s role is to translate general information into a patient-specific decision, linking evidence to the individual’s symptoms, imaging, frailty, renal function, medications, life expectancy, and goals.

72.4Global vascular access and a measured horizon scan

A major future challenge in vascular surgery is unequal access to basic and advanced care, alongside technological development. Essential vascular care in lower-resource settings is best understood as tiered capacity: prevention and cardiovascular risk reduction; diagnostic clinical assessment and ultrasound, with cross-sectional imaging where feasible; reliable medical therapy; basic operative capability with progressive development of endovascular services; supply chains for drugs, grafts, sutures, imaging, and devices; transport and referral systems; surveillance and follow-up; and training systems that retain a vascular workforce Consensus recommendations essential 2016. Framing the problem this way prevents a common mistake: buying a device, software platform, or imaging technology without the staff, maintenance, transport, medicines, follow-up, and referral structure needed to make it beneficial.

A tiered approach also helps clinicians decide what to prioritise. In many settings, the greatest immediate gain may come from smoking prevention, diabetes and hypertension control, antiplatelet and statin access, earlier recognition of limb-threatening ischemia, reliable duplex ultrasound, or referral links between district hospitals and specialist centers. Operative and endovascular capacity remains essential, but it is fragile if the patient cannot reach the center, if consumables are unavailable, if postoperative surveillance is impossible, or if no one can manage complications. Digital health can support referral, education, imaging consultation, and follow-up, but it should be matched to the tier that actually exists locally Consensus recommendations essential 2016. An AI triage tool that assumes universal imaging, stable connectivity, specialist availability, and rapid transport will not solve a system in which those components are missing.

Global vascular planning should therefore begin with a map of patient flow. Where are patients first seen? Who recognises vascular disease? Which medicines are reliably available? Which tests can be performed locally? How does a patient with acute limb ischemia, diabetic foot infection, ruptured aneurysm, stroke symptoms, or dialysis access failure reach a specialist? What happens after discharge? These are clinical questions, not only health-system questions, because the answer determines whether prevention, revascularization, amputation prevention, aneurysm repair, or surveillance can actually occur. A sustainable program usually grows by strengthening several tiers at once rather than by importing one advanced capability in isolation.

The same restraint should guide discussion of future AI. Recent reports on vascular computer vision, operative AI assistance, and future-facing AI applications describe active development and plausible uses in imaging, measurement, intraoperative support, documentation, education, and workflow triage Computer vision applications 2026 · Operative Role Artificial 2025 · Artificial intelligence magic 2024. Rather than concluding that AI has already improved outcomes across vascular surgery, the field now has a clearer set of candidate problems: repetitive measurement, image interpretation support, surveillance prioritisation, operative scene understanding, quality assessment, and earlier identification of patients who may otherwise be delayed.

The boundary between promise and proof should remain visible. For many future-facing applications, external validation, workflow effects, safety monitoring, equity performance, clinician workload, cost, and downstream patient outcomes are still unsettled Computer vision applications 2026 · Operative Role Artificial 2025 · Artificial intelligence magic 2024. A vascular team can be optimistic about these tools while still asking conventional clinical questions: Which patient benefits? What decision changes? What harm is possible? Who is accountable? What happens when the tool is wrong? What evidence would justify routine use? That posture keeps the service open to innovation without allowing novelty to outrun clinical judgment.