Scientific rigor before technological promises.
In biomedical research, exaggerated claims and premature promises can misdirect valuable scientific attention. We ground every technical decision in epistemic integrity and empirical humility.
EVIDENCE FIRST
“Scientific claims should be grounded in traceable evidence and reliable sources.”
Every computational observation produced by AlynaMOM is architected to point back directly to indexed peer-reviewed studies, clinical data repositories, or biological ontologies. We reject black-box assertions that cannot provide an auditable chain of citation.
- —Explicit citation mapping for every synthesized claim
- —Direct preservation of original source excerpts and context
- —Zero tolerance for fabricated references or hallucinated DOI markers
HUMAN OVERSIGHT
“AI-generated insights should be reviewed by qualified researchers before being used to support scientific conclusions.”
Automated systems excel at pattern recognition across high-dimensional text corpora, but they lack biological intuition, experimental wisdom, and contextual scientific skepticism. AlynaMOM is built to empower domain specialists, never to replace them.
- —Human-in-the-loop validation checkpoints across all workflows
- —Clear distinction between observed evidence and automated inference
- —Intuitive tools for researchers to reject, annotate, or calibrate model outputs
TRANSPARENCY
“We aim to communicate the capabilities, limitations, and development status of our technology honestly.”
We believe biomedical progress requires intellectual honesty. We do not disguise early-stage prototypes as finished clinical software, nor do we exaggerate our model performance with deceptive vanity metrics.
- —Open disclosure of early development and internal testing status
- —Explicit documentation of epistemic boundaries and known failure modes
- —No inflated marketing claims regarding clinical discoveries or drug patents
RESPONSIBLE AI
“Biomedical applications require particular attention to data quality, privacy, bias, and the limitations of automated reasoning.”
Biomedical data carries profound ethical responsibilities. Literature and genomic datasets can suffer from systemic historical biases, underrepresented patient populations, and publication bias toward positive results.
- —Rigorous monitoring for dataset sampling biases and cohort imbalances
- —Strict data hygiene: zero ingestion or storage of unconsented patient health data
- —Adherence to European GDPR standards and international bioethics guidelines
VALIDATION
“Computational outputs and generated hypotheses are starting points for investigation, not substitutes for experimental or clinical validation.”
A hypothesis generated by a neural network is merely a plausible candidate for investigation. It attains scientific validity only through rigorous bench experiments, in vitro and in vivo assays, and independent peer verification.
- —Clear marking of all hypotheses as unverified exploratory candidates
- —Support for researchers in designing reproducible wet-lab validation protocols
- —Alignment with established scientific standards for evidentiary proof
“Our ambition is to develop useful research tools through a careful combination of computational methods, scientific judgment, and responsible AI.”
AlynaMOM Research Initiative · Paris / European Union
AI as a research tool, not a substitute for medical judgment.
AlynaMOM is developing technology intended to support biomedical research. Its objective is to explore how artificial intelligence can assist with scientific information processing, literature analysis, and research hypothesis development.
AlynaMOM does not currently provide medical diagnosis, treatment recommendations, patient-specific clinical advice, or emergency medical services through this website.
AI-generated information may be incomplete, inaccurate, or misleading and must not be treated as independently validated scientific evidence.
Any future biomedical research application will require appropriate expert review, validation, and safeguards before being used in relevant research settings.