Mehedi Ahamed This workflow corresponds to n8n.io template #10997 — we link there as the canonical source.
This workflow follows the Form Trigger → Gmail recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"meta": {
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "fc08a16b-26b6-4346-9ad4-788da0e8c6af",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
16,
0
],
"parameters": {
"width": 560,
"height": 928,
"content": "## VLM Run Medical Assistant: Automated X-Ray Analysis \n\n### Overview \nThe **VLM Run Medical Assistant** uses VLM Run to automatically analyze X-ray images, detect abnormalities and generate diagnostic insights. It integrates directly into **n8n** workflows using **OpenAI-compatible API endpoints**, ensuring smooth setup and automation. \n\n### How it works \n- The workflow starts with a **file upload form**, where medical staff or users can submit **X-ray images**.\n- The uploaded file is passed to **VLM Run (model: `vlmrun-orion-1:auto`)**, which performs medical image interpretation and highlights potential disease areas. \n- A **Code node** extracts the resulting image URL from the model\u2019s output. \n- The workflow then automatically: \n - Sends results to the **doctor\u2019s email** for review. \n - Sends the same analysis and image to **Telegram** for instant notification. \n - Uploads patient data and reports to **Google Drive** for secure storage. \n\n### Requirements \n- **VLM Run API credentials** used to call the `vlmrun-orion-1:auto` model for X-ray analysis and generate disease-marked images. \n\n- **Gmail OAuth2 credentials** required to automatically email the diagnostic report to medical personnel. \n\n- **Telegram Bot credentials** used to send analysis results and annotated images directly to a Telegram chat. \n\n- **Google Drive OAuth2 credentials** needed to upload the patient\u2019s report and annotated X-ray file."
},
"typeVersion": 1
},
{
"id": "1e07426a-8d21-48a4-84fb-c09561489107",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
576,
432
],
"parameters": {
"color": 4,
"width": 480,
"height": 496,
"content": "## Send to Gmail & Telegram \n\n### How it works \n- The **Set node** provides two key fields: \n - `Description`: The disease diagnosis or \u201cNormal\u201d report from the model output. \n - `Output_Image`: The annotated image URL generated by VLM Run. \n- The **Gmail node** sends a detailed message containing both text and output image to the configured medical email address. \n- The **Convert to File node** transforms the description into a file format suitable for sharing. \n- The **Telegram node** sends the annotated X-ray and analysis summary as a document to the target chat or channel. \n\n\n"
},
"typeVersion": 1
},
{
"id": "993f85be-0090-4639-8594-c9ce5245907f",
"name": "Upload X-Ray Image",
"type": "n8n-nodes-base.formTrigger",
"position": [
-160,
768
],
"parameters": {
"options": {},
"formTitle": "Upload your data to test RAG",
"formFields": {
"values": [
{
"fieldType": "file",
"fieldLabel": "data",
"requiredField": true
}
]
}
},
"typeVersion": 2.2,
"alwaysOutputData": true
},
{
"id": "16fc2ea9-ebf7-4ebf-96a6-88e8e1a52ab8",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1056,
432
],
"parameters": {
"color": 6,
"width": 400,
"height": 496,
"content": "## Upload to Google Drive \n\n## How it works \n- The drive node uploads report to a designated Drive folder (e.g., `test_data`). \n- Each uploaded record can include both the **X-ray report** and the **annotated image**, enabling centralized storage for medical teams. \n\n"
},
"typeVersion": 1
},
{
"id": "f948a0cd-07bf-4b1f-9eb4-589974b23a3d",
"name": "Convert to Report",
"type": "n8n-nodes-base.convertToFile",
"position": [
1200,
944
],
"parameters": {
"options": {},
"operation": "toText",
"sourceProperty": "="
},
"typeVersion": 1.1
},
{
"id": "2e744214-3bb1-4eef-9925-609dfa7a9417",
"name": "Merge",
"type": "n8n-nodes-base.merge",
"position": [
400,
960
],
"parameters": {},
"typeVersion": 3.2
},
{
"id": "f01d95b4-4459-4765-8a13-7c7ecb48e560",
"name": "Analyze X-Ray",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
128,
768
],
"parameters": {
"text": "You are an expert in X-ray analysis and disease detection. Analyze the given X-ray, mark the affected area and give a disease description only if the patient has a disease, else analyze as a normal X-ray.. Give new detected image.",
"modelId": {
"__rl": true,
"mode": "id",
"value": "vlmrun-orion-1:auto"
},
"options": {
"detail": "high"
},
"resource": "image",
"simplify": false,
"inputType": "base64",
"operation": "analyze"
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.8
},
{
"id": "205c8790-3b92-48fd-9acf-5c952bcc8a28",
"name": "Send Details",
"type": "n8n-nodes-base.gmail",
"position": [
624,
784
],
"parameters": {
"sendTo": "mehediahamed@iut-dhaka.edu",
"message": "Here is the Patient X Ray Analysis report-",
"options": {
"attachmentsUi": {
"attachmentsBinary": [
{},
{}
]
}
},
"subject": "Patient X Ray Analysis"
},
"credentials": {
"gmailOAuth2": {
"name": "<your credential>"
}
},
"typeVersion": 2.1
},
{
"id": "b715ea5a-6d2e-453d-8789-434b5f4c0b0e",
"name": "Send Details1",
"type": "n8n-nodes-base.telegram",
"position": [
896,
784
],
"parameters": {
"chatId": "1872183963",
"operation": "sendDocument",
"binaryData": true,
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "f16181fa-f52e-4416-ab86-08104ac30e01",
"name": "Upload Report",
"type": "n8n-nodes-base.googleDrive",
"position": [
1200,
784
],
"parameters": {
"name": "Patient Info",
"driveId": {
"__rl": true,
"mode": "list",
"value": "My Drive",
"cachedResultUrl": "https://drive.google.com/drive/my-drive",
"cachedResultName": "My Drive"
},
"options": {},
"folderId": {
"__rl": true,
"mode": "list",
"value": "1S6baavqJn98MjUlbB6KtmARCWuWEekIZ",
"cachedResultUrl": "https://drive.google.com/drive/folders/1S6baavqJn98MjUlbB6KtmARCWuWEekIZ",
"cachedResultName": "test_data"
}
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "1a2fc9f9-f68f-46e6-915a-b229df345ce4",
"name": "Set Key",
"type": "n8n-nodes-base.set",
"position": [
896,
944
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "960e54b4-bf69-4154-9862-2a530709cf3d",
"name": "Description",
"type": "string",
"value": "={{ $json.choices[0].message.content }}"
},
{
"id": "71c9e67c-d02a-4cc2-9e72-930b03e83523",
"name": "Output_Image",
"type": "string",
"value": "={{ $json.storageLink }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "12f48efe-5caf-4a95-b607-5ff4692060c0",
"name": "Get Image Artifact",
"type": "@vlm-run/n8n-nodes-vlmrun.vlmRun",
"position": [
384,
768
],
"parameters": {
"objectId": "={{ $json.choices[0].message.content.match(/img_[a-zA-Z0-9]+/) ? $json.choices[0].message.content.match(/img_[a-zA-Z0-9]+/)[0] : '' }}",
"operation": "artifacts",
"sessionId": "={{ $json.session_id }}"
},
"credentials": {
"vlmRunApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
}
],
"connections": {
"Merge": {
"main": [
[
{
"node": "Send Details",
"type": "main",
"index": 0
},
{
"node": "Send Details1",
"type": "main",
"index": 0
},
{
"node": "Upload Report",
"type": "main",
"index": 0
}
]
]
},
"Set Key": {
"main": [
[
{
"node": "Convert to Report",
"type": "main",
"index": 0
}
]
]
},
"Analyze X-Ray": {
"main": [
[
{
"node": "Set Key",
"type": "main",
"index": 0
},
{
"node": "Get Image Artifact",
"type": "main",
"index": 0
}
]
]
},
"Convert to Report": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 0
}
]
]
},
"Get Image Artifact": {
"main": [
[
{
"node": "Send Details",
"type": "main",
"index": 0
},
{
"node": "Merge",
"type": "main",
"index": 1
}
]
]
},
"Upload X-Ray Image": {
"main": [
[
{
"node": "Analyze X-Ray",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
gmailOAuth2googleDriveOAuth2ApiopenAiApitelegramApivlmRunApi
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About this workflow
This workflow provides an automated pipeline for processing medical X-ray images using VLM Run (model: ), and distributing the AI-generated analysis to multiple channels—email, Telegram, and Google Drive.
Source: https://n8n.io/workflows/10997/ — original creator credit. Request a take-down →
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