Cloud AI vs. Local AI for Clinical Notes

Two ways to use AI for clinical documentation

AI documentation tools can reduce the time spent turning a conversation into a transcript and structured note. The main architectural choice is where that processing happens.

Cloud AI documentation

A cloud AI scribe sends audio, a transcript, or both to infrastructure operated by the vendor or its subprocessors. The service processes the material and returns a transcript, summary, or note. Some services retain source material; others offer configurable or short retention.

Local AI documentation

A local AI tool runs the transcription and note model on the clinician's computer. The recording does not need to be uploaded to a hosted transcription or generation service. A complete local product also needs secure storage, recovery, editing, retention, export, and update mechanisms. Running an isolated speech model is not a complete clinical workflow.

Why cloud processing can stop adoption

Clinical sessions contain information that is difficult to minimize after the fact. A single recording may include symptoms, diagnoses, trauma histories, medications, finances, relationships, workplace matters, and information about third parties.

Many cloud AI scribes require this information to pass through another service provider. That adds questions about contracts, subprocessors, storage regions, staff access, retention, deletion, training use, incident response, and whether the client can decline. Some employers and practices prohibit unapproved cloud tools. Others require a formal assessment before use.

These concerns do not mean every cloud tool handles information poorly. They mean the clinician must understand and accept where the software company sends and stores session information. When a provider cannot explain that path clearly, the time-saving benefit may not justify the risk.

Advantages of cloud AI

Larger and faster models

Cloud providers can run models that exceed the memory and compute available on an ordinary office computer. Hosted capacity can process work faster, but accuracy and note quality still depend on the model, audio, language, specialty, prompts, and evaluation method.

Integrations and team administration

Mature cloud products often connect directly to EHRs, scheduling systems, billing platforms, and team dashboards. Central administration can simplify deployment for larger practices.

Less local setup

A browser or mobile app can be easier to deploy than local models and runtimes. The vendor handles model hosting, capacity, and most updates.

Advantages of local AI

No upload for clinical processing

Local transcription and note generation do not send session content to a hosted AI provider. This reduces one class of third-party exposure involving vendor retention, subprocessor access, and hosted AI accounts. Backups, exports, EHR transfers, licensing, updates, and synchronized folders can still use network services.

Offline clinical workflow

Once models are installed, local clinical processing can continue without an internet connection. Commercial authorization and software updates may still require periodic access.

Direct control over records

A complete local product can give the clinician direct control over the device, retained records, and selected backup locations. The exact boundary depends on the product design. Astralcyte provides no support access to its local clinical vault.

Disadvantages and risks of cloud AI

Session content may be stored or processed by several organizations.
Retention and deletion may depend on vendor settings, contracts, and backup systems.
Foreign processing or remote access may apply even when primary storage is Canadian.
A vendor incident can affect many practices at once.
Service outages, account suspension, or contract changes can interrupt the workflow.
Clinicians must assess whether data is used for model training, evaluation, or analytics.

Disadvantages and risks of local AI

Local models can be slower or less accurate than leading hosted models.
Performance depends on available memory, processor capacity, and storage.
The clinician is responsible for device security and chosen backup locations.
A compromised unlocked computer can expose plaintext regardless of vault encryption.
Clipboard managers, screenshots, remote support, and synchronized folders can move data outside the vault.
If the software company cannot restore access, losing both the passphrase and recovery key can make records unrecoverable.

Cloud and local AI compared

Question	Common cloud deployment	Local deployment
Where AI processing runs	Vendor or subprocessor infrastructure	Clinician-controlled device
Session content sent to an AI vendor	Required by many services	Not required for local transcription and drafting
Internet needed for clinical processing	Required by many services	No, after model installation
Model scale	Can be larger and faster	Limited by local hardware
EHR and team integrations	Often available	Usually narrower
Vendor support access	Depends on provider controls	Depends on product design; local-only products may provide no vendor access
Primary security boundary	Vendor systems and clinician endpoint	Clinician endpoint and selected backups
Recovery after lost credentials	Vendor account recovery may be available	Depends on the local recovery design

What professional guidance expects either way

CRPO guidance asks clinicians to understand the system, assess privacy and security, minimize information, obtain meaningful consent where required, document AI use where appropriate, and review every output. Those duties apply whether the model is hosted or local. Local processing does not by itself determine compliance.

The CRPO guideline specifically addresses competence, privacy and security verification, client communication, the ability to decline, data minimization, record transparency, and review for hallucinations, bias, cultural insensitivity, and errors. IPC Ontario guidance for AI scribes emphasizes governance, vendor assessment, contracts, consent, retention, safeguards, and incident response.

Questions to ask before adopting a tool

What exact information leaves the device?
Who can access it, including subprocessors and support personnel?
Where is it processed, stored, backed up, and remotely accessed?
Is customer information used for training, evaluation, or product improvement?
How do retention, deletion, export, and account closure work?
How will clients be informed, and can they decline?
How are generated notes reviewed, corrected, and approved?

Which model is the better fit?

Cloud may fit when

the vendor and contract have been approved;
larger models or language coverage materially improve the workflow;
EHR integration and centralized administration are essential; and
the documented data handling is acceptable to the practice and its clients.

Local may fit when

vendor processing of session content is unacceptable;
offline clinical processing matters;
the clinician already has a practice-management system; and
the practice can manage endpoint security, recovery credentials, and backups.

Why Astralcyte Notes uses local AI

Astralcyte Notes is designed for clinicians who want documentation assistance but do not want cloud processing of session content. Transcription and note generation run on the clinician's computer. Retained transcripts and notes are stored in an encrypted local vault. Temporary audio remains in separately encrypted local recovery storage until transcription completes.

The tradeoff is deliberate. Astralcyte does not begin with the broad integrations or model scale of a cloud platform. It provides one focused workflow: capture the session, create the transcript, draft the note, review it, and keep the clinical record under local control.

See whether the local workflow fits your practice.

Review the three-step product workflow, hardware requirements, and device pricing.

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Sources and further reading

This article provides general product-selection information, not legal or professional advice.