Moonlight AI raises €2.8 million to accelerate AI-powered cancer diagnostics

Moonlight AI is raising €2.8 million as oncology diagnostics gradually enters a new industrial phase, where value no longer depends solely on genetic sequencing equipment or specialised laboratories, but increasingly on artificial intelligence models capable of extracting biological insights directly from medical images. This is the positioning adopted by the Swiss startup, which develops image analysis software for clinical diagnostics.

Founded at the intersection of hematology, pathology and artificial intelligence, the company develops software capable of transforming blood smear and cytology sample images into actionable diagnostic data. The objective is to help laboratories and physicians identify certain genomic biomarkers or pathological signatures associated with cancer more rapidly, without systematically relying on Next-Generation Sequencing (NGS).

This approach addresses a structural limitation of precision medicine. While targeted therapies increasingly depend on tumour genetic analysis, sequencing remains expensive, complex to deploy and often slow in hospital environments. Moonlight AI is specifically attempting to bypass this bottleneck by using computer vision to detect molecular information directly from images already produced in routine clinical workflows.

“Our technology enables labs to generate actionable, immediate results from slides they already use in their core workflows,” explained Christian Ruiz, CEO and co-founder of Moonlight AI. “By removing the need for expensive hardware or manual processes, we are empowering labs to scale their diagnostic capacity and deliver faster results to patients.”

From a technological standpoint, the company is positioning itself within the rise of AI models applied to digital pathology. In recent years, several academic studies have demonstrated that certain genetic mutations or tumour characteristics can be inferred from histopathological images, paving the way for a new generation of AI-assisted diagnostics. Moonlight AI is now attempting to transform these scientific advances into scalable clinical infrastructure.

The startup is currently focusing its developments on several diagnostically complex diseases, including myelodysplastic syndromes (MDS), non-small cell lung cancer and chronic lymphocytic leukemia. Part of the funding will also be used to accelerate regulatory work and commercial deployment.

One of the company’s main strategic assets lies in its proprietary dataset. Moonlight AI is currently building a library combining cytopathological imaging and genomic data, with the ambition of creating one of the first datasets of this kind at international scale. In medical AI, the quality and diversity of datasets are becoming critical factors both for model training and future clinical validation.

“By collaborating with an international consortium of clinical partners, we are building a dataset designed to ensure model robustness across real-world laboratory environments and diverse patient populations,” said Nicole H. Romano, CTO and co-founder of Moonlight AI.

The company is also seeking to expand this consortium in order to increase the clinical and geographical diversity of the data used to train its models. “The success of AI-based diagnostics fundamentally depends on the quality and diversity of clinical data,” added Dr Stefan Habringer, Chief Medical Officer and co-founder of Moonlight AI. “We are therefore opening our consortium to additional hospitals and laboratories that want to help shape the next generation of diagnostics.”

The funding round was co-led by Lotus One Investment, VP Venture Partners and MEDIN Fund, with participation from N&V Capital and existing investor QAI Ventures.