Diagnostics
6 Early-Stage AI Diagnostics Startups to Watch in 2026
DealBio tracks early-stage diagnostics startups across Europe and North America. These 6 use AI to turn complex biological and clinical data into information that can support diagnosis, monitoring and treatment decisions.
- AI Health
- Biomarkers
- Medical Imaging
- Precision Medicine
- Molecular Diagnostics
DealBio tracks early-stage diagnostics startups across Europe and North America. These 6 use AI to turn complex biological and clinical data into information that can support diagnosis, monitoring and treatment decisions.
The opportunity is increasingly moving from generating more data to making existing data clinically useful. Diagnostic value depends on whether a system can reduce interpretation time, surface a meaningful signal and fit the result into a decision a clinician already needs to make.
For early-stage startups, that shifts the evidence burden beyond model accuracy. The stronger commercial case comes when technical performance is paired with a clear user, a defined next action and a workflow that can be repeated across sites.
At a Glance
- EVIIVESwitzerland2022Immunotherapy response biomarkers
- CancerTwinSweden2026Cancer biomarker discovery
- CT ProvisionSpain2024Brain-haemorrhage outcome prediction
- AIMA BiohealthSpain2026Microbiome interpretation
- AinovisSpain2024Radiology report generation
- AuscultaNetSwitzerland2026Heart-murmur classification
01
EVIIVE
Developing blood tests that predict which cancer patients respond to immunotherapy.
- Country
Switzerland- Founded
- 2022
- Sector
- Diagnostics
- Funding stage
- Seed or earlier
- Founders
Why EVIIVE stands out
EVIIVE is developing a liquid-biopsy platform around a clear treatment-selection problem: identifying which cancer patients are more likely to respond to immunotherapy. The company has secured Venture Kick support and is a University of Zurich spin-off with clinical links to University Hospital Zurich.
Its platform analyzes extracellular vesicles, tiny particles released by cells, to capture dynamic immune signals. That gives EVIIVE a route to build companion diagnostics that can be tested against real treatment response rather than relying only on static tumor markers.
What EVIIVE is building
EVIIVE’s IMPASS platform profiles extracellular vesicles from blood and uses machine learning to identify immune subpopulations associated with treatment response. The company is starting with immuno-oncology, including melanoma.
A second layer, IMPACT, applies multi-omics analysis to the selected signals to investigate mechanisms, targets and resistance pathways. The commercial focus is precision diagnostics that help stratify patients before and during treatment.
Building digital-twin models to identify biomarkers for earlier, individualised cancer diagnosis and monitoring.
- Country
Sweden- Founded
- 2026
- Sector
- Diagnostics
- Funding stage
- Seed or earlier
- Founders
- Mikael Benson
Why CancerTwin stands out
CancerTwin builds on research from the Swedish Digital Twin Consortium and Karolinska Institutet. The underlying work combines clinical and multi-omics data to model disease mechanisms and identify biomarkers that may support earlier and more individualized cancer diagnosis.
The academic foundation is already linked to published cancer-biomarker research. That gives the startup a defined path for testing whether digital-twin models can produce biomarkers that outperform existing screening or monitoring signals.
What CancerTwin is building
CancerTwin builds computational models of tumors at population, subtype and individual levels. The models combine routine clinical information with multi-omics data, including spatial single-cell measurements, to prioritize biomarkers and disease mechanisms.
The initial commercial opportunity is diagnostics and monitoring, with the same modeling framework also able to surface potential therapeutic targets and patient-specific biological patterns.
Using CT scans and clinical data to estimate outcomes after brain haemorrhage.
- Country
Spain- Founded
- 2024
- Sector
- Diagnostics
- Funding stage
- Seed or earlier
- Founders
Why CT Provision stands out
CT Provision already has a working research-use product and retrospective clinical evidence. Its HOP software combines the admission CT scan with clinical variables to estimate complication and mortality risks after subarachnoid haemorrhage.
The core method is protected by granted US and European patents and is available through QMENTA for research use. That puts the startup at a useful stage for investors: the model can be evaluated in a real imaging workflow while the evidence base expands.
What CT Provision is building
HOP derives information from routine brain CT imaging and combines it with patient variables to estimate the likelihood of vasospasm, delayed cerebral ischaemia, poor functional outcome and death.
The product is designed for neurocritical-care risk stratification, helping teams identify patients who may require closer monitoring after the initial bleed.
Offering clinician-prescribed gut-microbiota testing with NGS analysis and AI-assisted clinical interpretation.
- Country
- Spain
- Founded
- 2026
- Sector
- Diagnostics
- Funding stage
- Seed or earlier
Why AIMA Biohealth stands out
AIMA Biohealth is taking microbiome testing into a professional workflow instead of leaving patients to interpret raw data. Its platform combines sequencing, biomarker analysis, reference data and AI-assisted interpretation for healthcare professionals.
More than 100 professionals already use the platform. That early usage matters because the investment question is not only whether microbiome data can be generated, but whether clinicians find the interpretation layer useful enough to make it part of repeated practice.
What AIMA Biohealth is building
AIMA offers 16S and shotgun metagenomic tests alongside related biomarker panels. The results are delivered through AIMA Clinic, where professionals can compare findings, review supporting evidence and prepare patient reports.
AIMA Care uses AI to suggest interpretations from the data loaded into the platform, but the healthcare professional reviews, edits and contextualizes the output before it is used.
05
Ainovis
Developing AI that analyses medical imaging and drafts structured radiology reports for clinician review.
- Country
- Spain
- Founded
- 2024
- Sector
- Digital health
- Funding stage
- Seed or earlier
- Founders
Why Ainovis stands out
Ainovis is targeting the reporting bottleneck in radiology rather than treating AI as a standalone diagnostic layer. The startup has raised €680,000 and is working with clinical partners while developing a system that converts imaging findings into structured report drafts.
Its human-in-the-loop design keeps the radiologist responsible for the final report. That makes workflow adoption, editing time and report consistency measurable signals of value alongside model performance.
What Ainovis is building
Ainovis supports radiologists with free-text or voice input, automatic template selection, structured draft generation and built-in checks. The system is designed to reduce time spent writing while preserving clinician control.
The product is aimed at hospitals and radiology organizations dealing with rising imaging volumes and constrained reporting capacity.
AI software turning a standard stethoscope into a cardiac screening tool for primary care, detecting heart valve disease missed in routine auscultation.
- Country
- Switzerland
- Founded
- 2026
- Sector
- Medtech
- Funding stage
- Seed or earlier
Why AuscultaNet stands out
AuscultaNet applies AI to one of primary care’s most familiar diagnostic tools. Its benchmark performance reached 91.7% AUROC, and the startup is preparing prospective validation against echocardiography while pursuing regulatory pathways in the United States, Europe and Switzerland.
The product is designed to work offline and with digital stethoscopes rather than requiring a new imaging system. That could make adoption easier if prospective clinical performance supports the benchmark results.
What AuscultaNet is building
AuscultaNet analyzes recordings from four standard cardiac auscultation sites and returns murmur detection, timing classification, grade estimation and a prioritized recommendation.
The software is intended to help primary-care clinicians decide which patients should be referred for echocardiography, while leaving the clinical decision with the professional.
Deal Sourcing for Investors
Looking for More Startups Like These?
DealBio helps investment teams discover, research, and monitor early-stage healthcare and life sciences startups aligned with their investment criteria.
Investment perspective
The investment case in AI diagnostics increasingly depends on where the model sits in the care pathway. A technically strong system that produces an isolated output still creates work for the user; a stronger product reduces a decision bottleneck and becomes part of an existing clinical routine.
That makes repeatability the more useful next signal. Beyond accuracy, investors can look for evidence that the same workflow works across clinicians, sites and patient groups, with limited customization and a clear handoff from AI output to clinical action. Products that prove that portability can build defensibility around implementation as well as the model itself.
Methodology
DealBio selected these six early-stage diagnostics and clinical decision-support startups because AI is central to how each product interprets complex health data for a defined healthcare decision. The selection spans blood biomarkers, digital twins, medical imaging, microbiome analysis and cardiac sound.
DealBio builds its Startup Briefs using publicly available startup materials, ecosystem sources, market references and professional profiles.