AI-Assisted Precision Care · Built for Oncology & Rare Disease

Evidence-based research. Personalized precision care.

AI generates answers. The Deliberation Engine™ challenges them — then human judgment turns them into a Defensible Decision™. Two pillars carry that work, for the cases where being right matters most.

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01 Open Research

Deliberation Engine™

AI finds more. Deliberation makes it stronger. In 30–60 minutes — the time a physician spends critically reading a single paper — the platform searches and synthesizes across thousands of sources, then puts every answer under red-team pressure.

500–2,000+

articles searched & screened

50–200+

registered trials summarized

10–30+

guidelines & consensus statements

Red team pressure

Challenge assumptions Test the evidence Red team review Risk & impact analysis Identify gaps Refine & reconcile

Step 1

AI Engine

Step 2

Deliberation Engine™

Step 3

Human Judgment

Outcome Defensible Decision™

02 Virtual Cell

Hypothesis-Driven Interpretations

A surviving hypothesis is not yet an answer. The Virtual Cell takes it into simulation — modelling the variant, the structure, and the mechanism in silico, so an interpretation is stress-tested before a patient ever carries the risk.

Variant → structure → function. Predicted folds and functional impact for the variants that actually matter.
Mechanism modelling. What the biology would have to do for this hypothesis to hold.
Treatment scenario simulation. Candidate strategies compared, with their expected trade-offs.
For, against, and unknown. Every hypothesis carries what supports it, what contradicts it, and what is still missing.

Step 1

Hypothesis

Step 2

Virtual Cell

Step 3

In-Silico Test

Outcome Data-Driven Interpretation

73 days

for medical knowledge to double

Projected doubling time of medical knowledge by 2020. In 1950 it was an estimated 50 years.
Densen P., “Challenges and Opportunities Facing Medical Education,” Trans Am Clin Climatol Assoc, 2011.

The problem is success

Experts aren’t failing. They’re being outrun by their own discoveries.

The volume of new research is not a sign that medicine is broken — it is a sign that medicine is working. But it has crossed a threshold. No clinician, however dedicated, can read, appraise, and apply everything that becomes relevant to a single complex patient.

That gap is where treatable patients get standard care instead of precise care.

AI is not the threat to human expertise. Information overload is. AI is the solution.

Divide and conquer

One patient. A panel of virtual experts. One synthesis.

AiM One Health assembles a panel of virtual experts and gives each one a specific learning assignment. They read in parallel, then collaborate to synthesize what they found — and the whole panel is convened again, from scratch, for the next patient.

Learning assignment

Literature & Evidence

Reads the current corpus for this indication — trials, reviews, case reports — and grades what it finds.

Learning assignment

Genomics & Variants

Interprets the molecular profile, resolves variants of uncertain significance, and maps them to mechanism.

Learning assignment

Trials & Access

Matches the patient against open trials and expanded-access pathways, with eligibility reasoning shown.

Learning assignment

Pharmacology

Checks agent selection, dosing, resistance mechanisms, and interactions against the full medication list.

Learning assignment

Guidelines & Deltas

Tracks what changed since the last guideline release — and flags where standard of care has moved.

Learning assignment

Red Team

Argues the other side. Every recommendation is challenged before it reaches a clinician.

Then they collaborate — and the panel reconvenes for every patient.

Individual findings are synthesized into one recommendation with the reasoning and the citations attached. The hard part isn’t running this once. It’s running it instantly, again, for the next patient — and the one after that.

The two halves of the platform

Research & Analysis. Virtual Cell.

One reads everything that is already known. The other tests what isn’t yet — in silico, before a patient carries the risk. Together they govern every case that moves through the platform.

Research & Analysis

Know everything that is known.

Continuous, patient-specific synthesis of the published record — appraised, graded, and traceable back to source.

Live literature synthesis — scoped to this indication, this biomarker, this patient.
Evidence grading — strength and quality stated, not implied.
Guideline deltas — what has changed since the last release, and whether it matters here.
Full citation trail — every claim clickable back to its source.

Virtual Cell

Test what isn’t known yet.

Model mechanism and treatment scenarios computationally, so hypotheses are stress-tested before they reach a person.

Virtual tumor boards — the full multidisciplinary deliberation, convened on demand.
Structure & variant modeling — predicted folds and functional impact for the variants that matter.
Treatment scenario simulation — compare candidate strategies and their expected trade-offs.
Hypothesis generation — data-driven research interpretations a human can then take further.

Demonstrated

Virtual tumor boards for precision cancer care.

A tumor board is the best mechanism medicine has for a hard case: assemble the specialists, make each of them argue their read, and reach a decision no single expert would have reached alone. Its limitation has always been scheduling.

AiM One Health convenes that deliberation virtually — the same multidisciplinary structure, available for every patient rather than the few who make the calendar. This is where we have proven the divide-and-conquer approach, and cancer therapy is where we are building it out first.

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Medical Oncology

Systemic therapy options & sequencing

Synthesized

Molecular Pathology

Variant interpretation & actionability

Synthesized

Radiology

Response assessment & burden

Synthesized

Clinical Trials

Eligibility & access pathways

Synthesized

Red Team

Challenges the emerging consensus

Challenged

Deliberation complete · reasoning and citations attached Illustrative

Open Research Ecosystem

The knowledge belongs to the community that creates it.

Every case that moves through the platform can make the next one better — but only if the resulting knowledge is held in common, validated openly, and governed for the long term.

The full loop · AiM One Health

Community-Owned Knowledge Base

Findings accumulate in a shared, structured commons — not locked inside a vendor’s black box.

Governance by AiM One Health

Clear stewardship over consent, provenance, contribution, and access — written down and enforced.

Trusted Validation & Peer Review

Nothing enters the commons unchallenged. Human review stays in the loop where it counts.

Sustainable Technology Infrastructure

Built to outlast any single grant cycle, model vendor, or funding round.

A question enters

A clinician brings a real case through the clinical interface, with consent and provenance intact.

The panel reasons and simulates

Research and analysis, then the Virtual Cell, produce a data-driven interpretation.

The commons gets smarter

Validated findings return to the community knowledge base — and feed the next patient’s panel.

A technology inversion

From machine learning to machine teaching.

For a decade we taught machines. The value now runs the other way: the machine’s job is to make the humans in the room measurably smarter — clinician and patient alike.

The last decade

Machine Learning

Humans supply the knowledge. The model gets better.

What comes next

Machine Teaching

The model supplies the knowledge. The humans get better.

“The value of AI is in teaching humans how to learn — and that matters more every year, because the speed of knowledge formation keeps increasing.”

AiM One Health

Precision Learning

Precision medicine requires precision learning.

Precision medicine is a team effort, and the patient is the most important member of the team. Yet patient education is still too often an After Visit Summary and a pamphlet — after which the learning journey moves to a search engine and a social feed.

The handoff is thin

A summary sheet and a pamphlet cannot carry a complex diagnosis, let alone a precision treatment plan.

The web is a storefront

Search results are a commercial enterprise. The ranking optimizes for revenue, not for this patient.

Social media rewards the wrong thing

Engagement favors the confident and the alarming. Misinformation travels further than nuance.

The result is a communications breakdown at every stage of care. Closing it takes two platforms working toward the same patient from different sides.

For the clinician

Clinical Intelligence Platform

AiM One Health gives the care team a panel of virtual experts per patient — research, analysis, and simulation synthesized into a recommendation with its reasoning and citations attached.

AiM One Health

For the patient & caregiver

Patient / Caregiver Intelligence Platform

Knowledge Advantage is building the other half — precision learning for the patient and the family caregivers who carry an enormous share of care for any chronic condition.

Knowledge Advantage

Start with one case

Bring us your hardest patient.

Submit a real clinical question and receive a Clinical Intelligence Report — the panel’s synthesis, the evidence behind it, and the reasoning you can challenge.

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