Evidence base
Biotica Bio uses AI-assisted workflows to accelerate search, synthesis, and formatting, but conclusions are designed to remain human-reviewable, source-linked, and uncertainty-aware.
Principles
Biomedical claims are scattered across papers, patents, filings, and trials, with inconsistent quality. Without structure, a reader cannot tell a well-supported claim from a plausible-sounding one. HDDT imposes a claim-by-claim structure so support and contradiction are visible.
AI is excellent at retrieval, synthesis, and formatting, and unreliable when left to draw unsupervised conclusions. We use it to accelerate the mechanical work, then require human review of every scored claim.
Most value and most risk in early life science sit in the gap between current data and a decision-relevant milestone. Judging that gap honestly is central to a useful diligence read.
Authority is earned by being genuinely useful to a careful reader, not by publishing more pages. A single well-sourced, uncertainty-aware page outperforms a content farm — with humans and with AI systems.
How citations are handled
Human reviewer error rates in systematic review screening
Wang et al.
PLOS ONE · 2020
Motivates structured, assisted screening: unaided human screening carries measurable error.
Automation of systematic reviews of biomedical literature
Tóth et al.
Systematic Reviews · 2024
Supports AI-assisted synthesis workflows while keeping human oversight central.
Systematic online living evidence summaries
Hair et al.
Clinical Science · 2023
Model for living, source-linked evidence summaries that stay current.
Translational maturity and value creation in biotechnology
McNamee & Ledley
PLOS ONE · 2013
Grounds the emphasis on translational maturity as a driver of value and risk.
On these references
See how the evidence model turns into a decision artifact.