AI Genogram Maker — The Emerging AI Category (2026)

Q: What is an AI genogram maker?

A tool that uses artificial intelligence, typically a large language model (LLM) or a purpose-built model, to generate a genogram chart from input — usually a natural-language description of a family, a transcript, or a questionnaire. Examples include GenogramAI, Cloudairy AI, and ConceptViz. The output is a visual chart showing family structure with annotations for the information the input described.

Q: Are AI genograms clinically accurate?

Accuracy depends on the quality of the input and the specific tool. AI-generated genograms are useful for rapid sketching and for drafting a starting chart that a clinician then edits. They should not be relied on as the final clinical record without human review — the same rule that applies to any AI-generated clinical content. For clinical genetics use where inheritance patterns drive testing decisions, the standard of care remains clinician-drawn and clinician-reviewed pedigrees.

Q: What is the difference between AI that makes a genogram and AI that reads a pedigree?

They are different classes of AI. AI genogram makers turn a description into a chart — input is text, output is a diagram. AI clinical interpretation (Evagene) takes an existing pedigree and generates a clinical narrative — input is a structured chart, output is a draft report covering findings, family implications, and recommendations. Both use large language models, but they serve different parts of the clinical workflow.

Q: Can I use an AI genogram maker in a clinical setting?

For note-taking and drafting, yes — many clinicians sketch with AI tools in preparation for a consultation or as a rapid way to capture a family from a referral letter. For the final clinical record, the chart should be reviewed and validated in a clinical pedigree tool that supports NSGC notation and feeds downstream risk analysis. Do not send patient-identifiable information to a consumer AI tool without confirming your service's data governance allows it.

Q: What are the limits of AI-generated genograms?

Ambiguous inputs produce ambiguous charts — a family description that says 'my grandmother had cancer' does not specify which grandmother or which cancer, and the AI will make a guess. AI tools vary in how faithfully they implement NSGC notation; some use generic family-tree symbols rather than clinical ones. Privacy is a concern for patient-identifiable input. Clinical interpretation of the chart (not just drawing it) is still a clinician's job.

Q: Does Evagene have an AI genogram maker?

Evagene offers two AI surfaces that both differ from a pure AI genogram maker. First, pedigree-image OCR — upload a scanned or photographed pedigree and Evagene reconstructs it as structured data. Second, AI clinical interpretation with bring-your-own-key LLMs — given a constructed pedigree, Evagene generates a structured clinical narrative covering key findings, family implications, data gaps, and screening recommendations. Evagene does not currently offer generation of a pedigree from free-text family description; that is a design choice to keep the chart under clinician control.

Q: What is BYOK and why does it matter for clinical AI?

BYOK — bring-your-own-key — means the clinical software sends AI requests using your own LLM provider account (Anthropic, OpenAI, or similar) rather than routing through the software vendor's account. Your organisation's contract with the LLM provider governs data handling, data residency, retention, and audit. This is important in clinical settings because it means the vendor of the clinical software is not a processor of your clinical text; the model provider is, under your existing contract.

Short version. "AI genogram" means two different things. (1) AI-generated charts: tools like GenogramAI, Cloudairy AI, and ConceptViz take a natural-language description of a family and produce a visual diagram. Useful for rapid sketching, limited by the quality of the input. (2) AI-generated clinical interpretation: a tool takes an existing structured pedigree and generates a narrative clinical report — key findings, family implications, data gaps, recommendations. Evagene offers the second class with bring-your-own-key LLM support. Both are useful; neither replaces clinician judgement, and the clinical setting determines which matters more to your workflow.

Two different classes of AI

AI for pedigrees and genograms in 2026 divides into two distinct problems:

Class	Input	Output	Representative tools
Chart generation	Free-text family description, transcript, questionnaire	A visual diagram with shapes and relationships	GenogramAI, Cloudairy AI, ConceptViz
Clinical interpretation	Structured pedigree data	A clinical narrative (findings, implications, recommendations)	Evagene AI interpretation
Image recognition	Scanned or photographed pedigree image	Structured pedigree data reconstructed from the image	Evagene pedigree OCR
Agent integration	LLM agent request (Claude, GPT)	Read / modify / analyse pedigree via tool calls	Evagene MCP server (11 tools)

These solve different problems. An AI chart generator makes the first draft of a diagram. An AI clinical interpreter reads a drawn chart and writes a clinical narrative. An image-recognition pipeline reconstructs a structured record from an existing picture. An AI agent integration lets an external AI assistant read or modify the pedigree as part of a larger workflow. They are not substitutes for each other.

AI chart generation: how it works

AI chart generators — GenogramAI, Cloudairy AI, ConceptViz, and increasingly also general-purpose tools like Claude and ChatGPT with diagram plugins — accept a free-text family description and produce a chart. Typical input:

"My mother's side of the family has a lot of breast cancer. Her mother died of breast cancer at 52. Her aunt (my great-aunt) had ovarian cancer in her 60s. My mother's sister had bilateral breast cancer at 45. I am 38, female, with no breast cancer diagnosis but I have noticed a lump. I also have two brothers, one older and one younger."

The AI parses the text, extracts individuals and relationships, and emits a diagram. The better tools use standard pedigree notation; less specialised tools fall back to generic family-tree shapes. The quality varies with input completeness, ambiguity, and whether the target chart is a therapy genogram (emotional relationships) or a clinical pedigree (affected status and age of onset).

Strengths. Speed. A working first draft in seconds from a referral letter, session transcript, or patient questionnaire. Useful for teaching, for quick case notes, and for pre-consultation sketching.

Limits. Ambiguous input produces ambiguous charts. "My grandmother had cancer" — which grandmother, which cancer, at what age? The AI guesses. Standard-notation fidelity varies between tools. Privacy matters: sending patient-identifiable family history to a consumer AI tool requires the same data-governance checks as any clinical text. Most AI chart generators do not feed downstream clinical risk models; the chart is a visual artefact, not a structured data record.

AI clinical interpretation: a different problem

The second class of AI — clinical interpretation — starts with an existing, structured pedigree (drawn by a clinician in a clinical pedigree tool) and generates a clinical narrative. The input is not free text; it is the structured data underlying the chart (individuals, relationships, affected status, age of onset, test results). The output is a draft report section covering key findings, inheritance pattern hypotheses, family implications, data gaps, and screening or testing recommendations.

This is a much more constrained problem than chart generation. The AI is not inferring who is related to whom; that is already known. The AI is reading the pedigree and articulating what a clinician would say about it, in a format suitable to be edited and sent into a clinical report.

Evagene implements this with bring-your-own-key (BYOK) LLM support. Your service provides its own Anthropic or OpenAI API credentials; Evagene sends the structured pedigree and its analysis prompts to your chosen model; the narrative returns to you. The Evagene vendor is not a processor of your clinical text — the LLM provider is, under your existing contract with them. See our cancer risk assessment guide for how AI interpretation sits alongside Bayesian risk models in Evagene's workflow.

Image recognition: reconstructing a pedigree from a picture

The third class — pedigree OCR — addresses a common real-world problem: a paper pedigree or a scanned image exists, and the service wants it in the clinical pedigree software as structured data. Evagene's pedigree OCR reconstructs the chart: parsing shapes, recognising affected status, reading annotations, and rebuilding the family structure in Evagene's data model.

This is an image-recognition problem with clinical-notation prior knowledge, not pure LLM generation. It complements the other AI classes — once the chart is structured, AI interpretation can run on it, AI agents can query it, and the chart lives in the same data store as pedigrees drawn from scratch.

Agent integration: MCP and the "AI assistant reads my pedigree" workflow

A newer class again is agent integration: exposing pedigree tools to an AI agent like Claude Desktop or Claude Code via the Model Context Protocol (MCP) so that the clinician's AI assistant can read, modify, and analyse a pedigree directly. Evagene ships an MCP server with 11 pedigree tools. A clinician asks their AI assistant something like:

"Look at the pedigree for the Smith family in Evagene. What inheritance patterns are compatible with the phenotype distribution? Draft a paragraph for the clinical letter."

The AI assistant calls the Evagene MCP tools to fetch the pedigree, runs the pattern identifier, and drafts the paragraph. This is a different clinical AI surface from a direct "AI interpretation" button — it puts the pedigree at the disposal of a broader AI workflow that the clinician manages.

Main AI chart-generation tools

GenogramAI. Purpose-built AI genogram generator aimed at family therapists. Emphasises genogram-style emotional-relationship vocabulary. Accepts text descriptions and produces a genogram chart with annotations.
Cloudairy AI. General-purpose AI diagram tool that includes genogram and family-tree modes. Broader diagramming capability beyond genograms.
ConceptViz. AI visualisation tool that includes family-tree and pedigree generation among other diagram types. Useful for teaching contexts.
General LLMs (Claude, ChatGPT). Describe a family and ask for a genogram in text form (ASCII diagram or description that can be drawn in a pedigree tool). Fast for sketching, limited by the LLM's symbol knowledge.

Where the limits are

Clinical accuracy. AI chart generation is a drafting tool, not a replacement for clinician-drawn pedigrees. For teaching, for rapid note-taking, for drafting a chart that a clinician will then edit, it is useful. For the clinical record on which testing decisions are based, the chart should be constructed or verified in a clinical pedigree tool with structured data and NSGC notation enforcement.

Privacy. AI services handle input text according to their own data policies. Consumer AI tools may or may not train on user input; vendor AI tools under enterprise agreements generally do not. Before sending patient-identifiable family history to an AI tool, confirm: (a) your organisation's data governance policy permits it; (b) the AI vendor's contract covers clinical data handling appropriately; (c) the input is minimised to what the AI needs.

Standard notation. Tools vary in how faithfully they implement NSGC pedigree notation versus generic family-tree symbols. For clinical use, NSGC-compliant output is expected; general-purpose AI tools may not meet that bar without specific prompting.

Clinical interpretation versus chart drawing. An AI that drew the chart did not interpret it clinically. Inheritance pattern, cancer risk, variant classification, testing recommendation — these require clinician judgement or, with appropriate safeguards, AI clinical interpretation tools that run on the structured chart.

How Evagene supports this

Evagene's AI strategy is different in class from the chart-generation tools. Rather than generating a chart from text, Evagene provides three AI surfaces that each serve a structured-data workflow:

Pedigree image OCR. Upload a scanned or photographed pedigree, Evagene reconstructs it as structured data. The chart becomes a first-class record, not a static image.
AI clinical interpretation with BYOK LLMs. Given a constructed pedigree, Evagene generates a structured clinical narrative covering key findings, family implications, data gaps, and screening recommendations. BYOK means your LLM account (Anthropic, OpenAI) handles the traffic under your existing contract; Evagene stores keys encrypted at rest with Fernet. Analysis Templates let a service codify its house style of report-writing and reuse across cases.
MCP server with 11 pedigree tools. AI assistants (Claude Desktop, Claude Code, and any MCP-compatible agent) can read, modify, and analyse Evagene pedigrees as part of broader clinician workflows. A clinician can ask their AI to pull a case, run a pattern check, and draft a report section — without leaving their AI assistant.

Evagene does not currently offer AI generation of a pedigree from free-text family description. This is a deliberate design choice: for the clinical record, the chart should be clinician-constructed (or OCR-reconstructed from a clinician-drawn original), not LLM-inferred from ambiguous text. For teaching, drafting, or pre-consultation sketching, dedicated AI chart-generation tools are the right choice and the chart can be imported into Evagene via OCR once finalised.

For services whose clinical AI needs centre on interpretation rather than drawing, Evagene's combination of BYOK clinical interpretation, Analysis Templates, and MCP agent integration covers most practical cases. For services whose needs centre on rapid text-to-chart sketching — particularly in therapy and teaching contexts — a dedicated AI genogram maker complements rather than competes with Evagene.

Frequently asked questions

What is an AI genogram maker?

A tool that uses AI, typically a large language model, to generate a genogram chart from a natural-language description or other input. Examples include GenogramAI, Cloudairy AI, and ConceptViz.

Are AI genograms clinically accurate?

Useful for rapid sketching and drafting; should not replace clinician-drawn charts in the final clinical record without review. Accuracy depends on input quality.

What is the difference between AI that makes a genogram and AI that reads a pedigree?

Chart generation turns text into a diagram. Clinical interpretation turns a diagram into a narrative report. Different classes of AI for different parts of the workflow.

Can I use an AI genogram maker in a clinical setting?

For note-taking and drafting, yes. For the final clinical record, chart should be reviewed in a clinical pedigree tool. Confirm patient-data governance before sending identifiable info to a consumer AI.

What are the limits of AI-generated genograms?

Ambiguous input produces ambiguous charts. Standard-notation fidelity varies. Privacy requires governance. Clinical interpretation remains a clinician's job.

Does Evagene have an AI genogram maker?

Evagene offers AI clinical interpretation (BYOK), pedigree-image OCR, and MCP agent integration — not text-to-chart generation.

What is BYOK and why does it matter for clinical AI?

Bring-your-own-key. Your own LLM account handles the traffic; the clinical software vendor is not a processor of your clinical text. Important for clinical data governance.

AI genogram maker: the emerging AI category