Use Cases and Clinical Scenarios

Start with a practical workflow, then grow into your specialty. Every scenario combines a medical AI model with your own protocols, and keeps a clinician in control of every output.

Four Workflows to Start With

Lab Reports to Structured Results

Turn clinical documents into structured, checked data.

The problem: Lab results arrive as scanned letters, faxes and PDFs in different layouts. Staff retype values by hand, which takes time and introduces errors.
What Akasiio does: Reads each report, extracts every value into structured fields, and reads each value twice independently. Any mismatch or uncertainty is flagged for review before it reaches the record.
Who benefits: Doctors’ assistants, nurses and specialists who process incoming results.
Data involved: Lab reports containing patient data, processed only inside your account.
How it is evaluated: Numeric accuracy against manually verified reports, plus the share of values flagged and how often those flags were justified.

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Protocol Questions

Answers from your own protocols, with the page they came from.

The problem: Protocols and guidelines are long, spread across systems and updated often. Finding the right paragraph during a busy clinic is slow.
What Akasiio does: Answers questions in plain language using only your own documents. Each answer links to the document, page and passage it came from, and users only see documents they are allowed to access.
Who benefits: Specialists, residents and nurses, especially new staff learning local ways of working.
Data involved: Your protocols and guidelines; no patient data is needed for this workflow.
How it is evaluated: Clinicians rate answers for accuracy and missed information, and every citation is checked against its source.

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Pre-Visit Summaries

The patient’s story, ready before the consultation.

The problem: Specialists spend valuable minutes before each consultation reading referral letters, history and prior results.
What Akasiio does: Gathers the referral, history and previous results and writes a concise summary in your house style, highlighting what changed since the last visit.
Who benefits: Specialists and their teams preparing consultations.
Data involved: Referral letters, history and results, processed only inside your account.
How it is evaluated: Clinicians rate completeness and accuracy; preparation time is measured against your baseline.

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Dictation to Notes

Speak naturally; review a structured draft.

The problem: Clinicians dictate or type notes after each consultation, often at the end of the day.
What Akasiio does: Transcribes dictation in English or a leading European language inside your account and drafts a structured note. Uncertain drug names, doses and dates are flagged; the clinician reviews and signs.
Who benefits: Specialists and GPs who document after every consultation.
Data involved: Audio and notes containing patient data, processed only inside your account.
How it is evaluated: Transcription accuracy on your clinicians’ own recordings, and the share of drafts accepted with no or minor edits.

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See What an Answer Looks Like

Every output separates what the model observes from what it infers, states its confidence, cites your own protocol, and names the clinician’s next step.

Input

CT thorax, 280 slices

Clinician

Draft the findings for this CT thorax in our template.

Akasiio Draft for clinician review

Observation: Lungs: no focal consolidation. Small left pleural effusion. Mediastinum within normal limits.
Interpretation: Findings consistent with a small left-sided effusion; no other acute abnormality identified.
Confidence: Moderate
Grounded in your protocol: Formatted in your CT thorax reporting template (Radiology templates, v4, section 2).
Clinician action: Radiologist reviews the images, edits the draft and signs the report.

Input

Scanned lab report (Dutch units)

Laboratoriumuitslag Synthetic example

Test	Result	Ref.
Hemoglobine	6.8 mmol/L	8.5–11.0
Kalium	6.1 mmol/L	3.5–5.0
eGFR	38 mL/min	>60
CRP	4 mg/L	<10

Clinician

Extract this lab report and flag anything urgent.

Akasiio Draft for clinician review

Observation: Four values extracted and read twice independently; both readings agree. Hemoglobin low, potassium high, eGFR low, CRP normal.
Interpretation: Potassium above your escalation threshold; hemoglobin and eGFR outside reference ranges.
Confidence: High for extracted values (both readings agree)
Grounded in your protocol: Your escalation protocol: potassium above 6.0 mmol/L needs same-day review (Internal medicine protocols, section 3.2).
Clinician action: Potassium flagged for same-day review; the doctor decides next steps.

Illustrative examples with synthetic data. These are not real patient data or real model output; they show the format of Akasiio’s answers. Document names and sections are examples of your own protocols.

Scenarios by Specialty

The same open medical models every provider can download, made yours by grounding them in your own protocols, templates and criteria. That is the difference your clinicians will notice.

Pilot-ready Text and document scenarios that can start as a supervised pilot. Validation first Imaging scenarios that need local validation on your own cases before clinical use, and may fall under medical-device rules depending on intended use.

RadiologyValidation first

“Has anything changed since the last X-ray?”

The model: Longitudinal imaging and anatomical localisation: compares a chest X-ray with prior images.
Your knowledge: Your department’s reporting templates and follow-up protocols.
What the clinician gets: A draft comparison with the regions of change marked, in your report structure, for the radiologist to confirm.

RadiologyValidation first

“Give me a first draft of the structured report.”

The model: High-dimensional imaging: reads CT and MRI studies.
Your knowledge: Your structured reporting templates and preferred terminology.
What the clinician gets: A draft findings section in your template, which the radiologist edits and signs.

PathologyValidation first

“Where should I look first on this slide?”

The model: Histopathology: processes whole-slide images.
Your knowledge: Your lab’s reporting protocol and grading conventions.
What the clinician gets: Highlighted regions of interest and a draft description; the pathologist decides.

DermatologyValidation first

“Does this lesion meet our biopsy criteria?”

The model: Image classification for dermatology.
Your knowledge: Your clinic’s referral and biopsy criteria.
What the clinician gets: A suggested category with the matching step from your protocol, for the dermatologist to decide.

OphthalmologyValidation first

“Pre-sort today’s retinal screening images.”

The model: Image classification for ophthalmology.
Your knowledge: Your screening protocol and grading scheme.
What the clinician gets: Screening images pre-graded and sorted, with flagged ones first for the ophthalmologist’s review.

Internal medicinePilot-ready

“Which of these results need action today?”

The model: Medical document understanding: extracts structured data from lab reports.
Your knowledge: Your reference ranges, escalation rules and follow-up protocols.
What the clinician gets: Structured results, with values outside your thresholds flagged against your own protocol.

Intake and triagePilot-ready

“Summarise the intake before I see the patient.”

The model: Medical question answering for pre-clinical interviews and triage support.
Your knowledge: Your intake questionnaires and triage protocol.
What the clinician gets: A structured intake summary with a protocol-based urgency suggestion, which the nurse or doctor confirms.

Every specialtyPilot-ready

“What does our protocol say for this patient?”

The model: Medical question answering.
Your knowledge: Your protocols, care pathways and formulary.
What the clinician gets: An answer grounded in your documents, with a citation to the exact page.

Model capabilities as published for the open MedGemma models. Akasiio supports clinical decisions; it does not make them.

Discuss a Scenario for Your Specialty