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Industry — 05

Healthcare

Anything entering a clinical workflow has to be safe first.

call-centre load
-%58call-centre load
patient app downloads
1.4Mpatient app downloads
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The biggest value of AI in healthcare is not diagnosis — it is taking administrative load off clinicians. Automating scheduling, documents, coding and patient messaging returns physician time to patients. Where patient data is involved, the architectural answer is often on-premise.

In short

How is AI used in healthcare?

The biggest gain from AI in healthcare is administrative rather than diagnostic: scheduling and call-centre automation, summarising clinical notes, medical coding support, patient communication and treatment reminders. Image analysis does not diagnose — it marks a suspicious region and sets priority, and the final call stays with the clinician. Neuros usually deploys these inside the hospital network, so patient data never leaves it.

What's hard in this sector

Anything entering a clinical workflow has to be safe first.

01

Data sensitivity

Patient data is a special category; plenty of scenarios simply cannot leave your own infrastructure.

02

System fragmentation

HIS, lab, imaging and scheduling all speak separately; without an HL7/FHIR layer there is no integration.

03

Clinical safety

Under uncertainty the system must escalate, not decide. That is a product requirement, not a preference.

What we can build for you

  • 01A patient app: booking, results, prescriptions and payment
  • 02Scheduling and contact-centre automation
  • 03Clinical note summarisation and medical coding support
  • 04Imaging analysis and pre-read (radiology, pathology)
  • 05Patient messaging and adherence reminders
  • 06On-premise AI deployment where data cannot leave

Sources

  1. 01HL7 FHIR R4 — Sağlık verisi birlikte çalışabilirlik standardıHL7 International · 2019
  2. 026698 sayılı Kişisel Verilerin Korunması KanunuT.C. Mevzuat Bilgi Sistemi · 2016
  3. 03Kişisel Veri Güvenliği Rehberi (Teknik ve İdari Tedbirler)Kişisel Verileri Koruma Kurumu · 2018
  4. 04Regulation (EU) 2024/1689 — Artificial Intelligence ActAvrupa Birliği Resmî Gazetesi · 2024

Frequently asked

Questions we get asked

Get in touch

Not unless it has to. The default in Neuros healthcare projects is an on-premises model: the server sits inside the hospital network, no external service is called and records stay within the institution. If cloud is used, region, access rights, encryption and retention are written up front as architectural decisions, with additional safeguards contracted for special-category data.

Yes. Neuros integrates over HL7 FHIR; where the system does not support FHIR, an adapter layer is written over HL7 v2 messages or database views. The connection always starts read-only: the data flow is verified first, then write-back is enabled. That order keeps an integration fault from touching the clinical record.

Systems built by Neuros do not diagnose; they produce a first pass and present findings to the clinician with their source. Image analysis marks a suspicious region, proposes a priority order and shows its confidence score — the final decision is always the doctor's. That separation is required both for clinical safety and for the EU AI Act's human oversight obligation.

Other industries

Let's assess your sector specifically

This page is the general frame. Let's find the actual bottleneck in your operation together, in a 30-minute call.

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