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5C Network

Bengaluru-based AI-native radiology platform combining AI workflow and report drafting with a network of specialist radiologists.

Company overview

5C Network operates a radiology reporting platform for hospitals and diagnostic centres in India. Its Bionic system uses AI to run the reading workflow and draft reports, which are signed by the company's specialist radiologists. The company states it reads over 20,000 scans a day for more than 2,000 hospitals across India.

Headquarters
Bengaluru, India
Founded
2017 source
Company type
AI Technology Developer, Healthcare IT Provider, Platform Provider
Deployment
cloud

In its own words: “5C Network is the radiology partner that reads 20,000+ scans a day for 2,000+ hospitals across India — AI runs the workflow, 400+ specialist radiologists sign every report.” source

AI products and platforms

  • BionicAI-powered

    Clinical AI engine and radiology operating system for AI-assisted radiology reports and workflow

    Product page ↗
  • SecondReadAI-powered

    Independent second opinion service on scans

    Product page ↗

Technology categories

Clinical applications

  • teleradiology reporting
  • report drafting

Markets served

  • India

Official sources

Last verified 29 October 2026 against the sources above.

Questions about 5C Network

How does an AI scribe work?

An AI scribe typically works in four steps:

  • Capture: a phone, computer or room microphone records the consultation after the patient is informed or consents.
  • Transcription: speech recognition converts audio to text and separates speakers.
  • Summarization: a language model extracts relevant content and drafts a note in the chosen format, such as a SOAP note, sometimes with codes or orders suggested.
  • Review: the clinician edits and signs the draft, which is then filed in the EHR, often through an integration.

Each step can introduce errors, so final responsibility for the note stays with the clinician. Ask vendors whether audio is stored, whether data is used to train models, and how fabricated or missing content is measured.

How is radiology AI different from general diagnostic imaging software?

General diagnostic imaging software stores, displays and manipulates images, for example PACS viewers, 3D reconstruction or measurement tools operated by the user. Radiology AI adds automated analysis that produces its own output, such as a detection, triage flag or quantitative result.

The distinction matters for evaluation:

  • Viewing and processing tools are judged mainly on image quality, usability and integration.
  • AI analysis tools also need evidence on accuracy, populations studied, failure modes and monitoring.

Both can be regulated medical devices depending on intended use; in the US many image management and processing functions fall under device classifications. Many vendors now combine the two, so ask which functions are AI-based and which are covered by a regulatory authorization.

What is “hallucination” in generative AI tools used in healthcare?

Hallucination is when a generative AI tool produces content that sounds plausible but is false or unsupported by its inputs, such as a symptom, medication or exam finding that was never mentioned in a consultation.

It matters most in documentation, summarization and question-answering tools, because errors can be copied into the medical record. Common safeguards are:

  • Clinician review and sign-off of every generated note or summary.
  • Links from generated text back to the source audio or record.
  • Vendor testing that measures omission and fabrication rates in realistic conditions.
  • Clear policies on what the tool must not be used for.

WHO's guidance on large multi-modal models highlights inaccurate or false outputs as a key risk.

More questions in the FAQ hub

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