The Architecture of Certainty
What Clinical AI can learn from Algorithmic Trading.
In the rush to adopt Artificial Intelligence in healthcare, the industry is currently fixated on Generative AI. The promise of Large Language Models (LLMs) that can "read" patient notes and suggest diagnoses is alluring.
But in the high-stakes world of financial technology, we learned a hard lesson decades ago: Probability is excellent for discovery, but it is dangerous for execution.
In modern algorithmic trading, there is a fundamental architectural distinction between finding a signal and executing a trade. It is a distinction that Clinical AI must adopt if it hopes to move from "experimental tool" to "standard of care."
Signal vs. Execution
In advanced financial systems, we rarely allow a Machine Learning model to execute orders blindly. ML is probabilistic—it deals in "likelihoods." It is fantastic at scanning millions of data points to find a pattern (the Signal) within the noise.
However, we do not let a probabilistic model control the bank account directly. If the model "hallucinates" a trend, capital is lost.
Instead, that Signal is passed to a Deterministic Logic Layer. This is a rigid, rule-based engine that checks the signal against strict risk parameters and compliance protocols.
- The Signal asks: "Is there an opportunity?" (Probabilistic)
- The Execution asks: "Does this meet the rules?" (Deterministic)
The "Black Box" Problem in Medicine
Currently, many diagnostic AI tools attempt to use Neural Networks for the entire process. They feed patient data in and ask the "Black Box" to predict a diagnosis.
In complex fields like Haematology—where a diagnosis requires the synthesis of genetics, flow cytometry, and molecular data—this is risky. A diagnosis is not a prediction; it is a classification based on strict international criteria (such as WHO or ICC guidelines).
If we apply the Financial Architecture to medicine, we get a safer, more robust model:
1. The Signal
AI Extraction
2. The Execution
Deterministic Logic
3. The Validation
Human-in-the-Loop
How it works:
- →Ingest: AI reads messy, unstructured lab reports from disparate sources. It identifies the "Signal"—parameters like NPM1 mutation status or CD34+ blast percentage—but makes no clinical decisions.
- →Verify: Extracted data is fed into a Logic Engine. This layer does not "guess." It strictly applies the codified rules of clinical guidelines (WHO/ICC). Transparent. Auditable. A "glass box" rather than a black box.
- →Audit: Just as a trader oversees an algorithm, the Clinician remains the final arbiter. The system presents the derived logic and prompts the human only for missing context (e.g., clinical history).
Certainty is the Product
In both finance and medicine, an audit trail is a legal requirement. But in medicine, the stakes are not capital—they are lives.
By separating Signal (AI) from Execution (Logic), we can build diagnostic systems that harness the speed of automation without sacrificing the safety of strict adherence to medical guidelines.
We are building this architecture at Haem.io.
Multi-Modal Input
Ingests genetics, cytogenetics, and clinical history from diverse formats.
AI
NLP data extraction from unstructured reports.
Logic Core & Precision Diagnosis
Provable WHO/ICC logic delivers exact disease classification.