MEDILIT

Capture, redaction, note generation, the self auditing pass, and letter and document generation are all model output, delivered through a multi agent architecture the company describes as its core technology. No human scribe tier, no services layer and no host platform that the AI is a feature of.

The only checking mechanism described is the system auditing ITSELF. No human review gate, sign off step, confidence threshold, abstention behaviour or escalation path was located anywhere in the retrieved materials. This index has ruled on this pattern before, holding that a machine confidence or quality primitive is not an oversight mechanism: SimboAlphus earned transparency credit for exposing a per note quality score and was still graded down on autonomy because a score says nothing about default behaviour or whether a human is required. The same applies more strongly here, because a self audit run by the same system that produced the output is an internal consistency check rather than independent verification. The likelihood is that clinicians do review notes before use, since that is how every product in this category works in practice, but the vendor does not say so and this index grades what is published.

Graded B for describing how the system is built, which most of this tail will not do. MEDILIT names a MULTI AGENT architecture with a SELF AUDITING layer intended to catch omissions and hold consistency across long consultations, and separately names its compute platform through a published AMD case study documenting the product running on AMD Ryzen Embedded 8000 Series processors. Naming both the agent design and the silicon is more disclosure than almost anything else at this scale offers. Held at B because no model card, named language model, accuracy figure, error rate or evaluation methodology exists for any of it, and because the product page headline calls it the most accurate AI medical scribe without publishing a single number to support the superlative. A self auditing claim with no measured catch rate is an architecture description, not a result.

No study, controlled evaluation, accuracy benchmark, third party clinical assessment, named customer or deployment count located. The AMD case study is a genuine externally published document and is more than most vendors at this scale have, but it is a technology partner marketing artifact rather than clinical evidence: it describes the solution as making note taking more efficient and allowing doctors to save time, with no measurement, cohort or comparator. Treat it as confirmation that the product exists and runs on named hardware, not as evidence of performance.

The strongest privacy architecture found in the long tail of this wave, and it is a design decision rather than a policy statement. Transcripts are REDACTED BEFORE ANY ANALYSIS: names, dates of birth, numbers and email addresses are replaced with random values before content is sent for processing, so identifiers never reach the generation layer and no personally identifiable information is linked to the data being analysed. That removes a risk by construction in the same way this index credited voize for on device inference and Veradigm for claiming an audio artifact that never exists. Encryption is stated in transit, at rest, and after processing, which is a more complete statement than the usual in transit and at rest pairing. Held at B rather than A because three questions central to this axis are unanswered: whether audio is retained and for how long, whether customer content is used to train models, and how robust the redaction is against identifiers that appear conversationally rather than as structured fields, which is the hard case for any de identification pipeline. Scribeberry holds the reference position on this axis because it answers all of those explicitly.

Not assessed, and the reason is worth stating rather than leaving as a blank. No HIPAA statement or business associate agreement reference was located. Pricing quoted excluding GST indicates the company sells into a GST jurisdiction rather than the United States, in which case HIPAA is not the governing regime and its absence is expected rather than alarming. A buyer should establish which privacy framework the vendor actually operates under, and a US buyer in particular should confirm that HIPAA obligations and a BAA are available at all before proceeding.

Not assessed. No named or dated certification, audit report, penetration test or trust centre located. The encryption and redaction commitments are described controls rather than attested ones, and the distinction matters: a redaction pipeline is exactly the kind of claim an independent assessment would test.

Not a regulated medical device and none claimed or required for documentation. No diagnostic suggestion, coding automation or order drafting was found in the retrieved materials, so the decision adjacent surface that puts other vendors in this lane on the software as a medical device watchlist is not present here.

One real governance positive and one complete gap. The positive is structural: choosing to strip identifiers before the analysis layer is a data governance decision taken at design time rather than a control bolted on afterwards, and it limits what the vendor itself can see. The gap is that no fairness statement, subgroup analysis, accent or dialect performance disclosure, or model limitations document was located, while the product markets itself on accuracy and on handling complex and lengthy consultations. Notably absent, and worth recording as a positive contrast, is any revenue, coding intensity or reimbursement optimisation language, which keeps this vendor off the coding gradient this index tracks across much of the rest of the lane.

Not assessed. No integration mechanism, named EHR, API, standard or even a transfer workflow was located. For a product that generates notes, letters, referrals, reports and plans, where those documents end up is a first order question, and the answer is not published. Ask whether the output writes back to a record system, exports as a file, or is copied by hand, because the workflow cost differs enormously between them.

Partially described, with one detail worth chasing. The company refers to its own secure cloud servers, so a hosted service is the baseline. But a published AMD case study documents the solution running on AMD Ryzen Embedded 8000 Series processors, and embedded silicon points toward local or edge inference rather than pure cloud processing. If clinical audio is processed on hardware inside the practice, that would place MEDILIT alongside voize in the small group of vendors where the data physically does not leave the site, which would be a materially stronger position than the grade here reflects. It is graded C rather than higher because the vendor itself does not describe a deployment model, and no hosting region, residency option or sub processor detail was located. Ask directly where inference runs.

A published two tier ladder with the gating stated, which is better than most of this lane. Standard and Premium both include the AI scribe plus letter and custom document generation, and Premium adds unlimited AI document generation and additional AI assistant tools, so a buyer can see exactly what the upgrade buys before talking to anyone. The trial terms are unusually clean and stated plainly: the trial grants full access to all premium features, and no payment information is required to start, which removes the card capture pattern common at this tier. Prices are quoted excluding GST, so the buyer should confirm the tax inclusive figure for their jurisdiction. Held at B rather than A because enterprise or multi clinician terms are not addressed and no per seat versus per practice basis is stated.

General clinical use with breadth in document TYPES rather than in specialties. The output range is genuinely wide for a small vendor: consultation notes, letters, referral documents, patient instructions, reports, plans and user defined custom documents, with the design explicitly aimed at long and complex consultations rather than short visits. What is missing is specialty depth. No specialty is named, no specialty specific instrument, form or scoring system is referenced, and no care setting beyond general consultation is addressed. On this index's test, naming the instrument is what distinguishes real domain work from a general model with a template, and nothing here does that.