AI app for healthcare · no coding needed
Personal health evidence and reporting workspace
Reduce the effort of turning scattered health data into reviewed, plain-language answers and plans.
Made for: Individuals combining wearable, log and lab data who want plain-language answers and plans
What it does for you
The problem
Health data sits in separate apps and wearables, so people cannot see patterns or get clear answers about their own records.
What it gives you
User-reviewed health summaries linked to source records
What you give it
Connected wearable datamealhabit logslab resultssymptom notes
Build your own version of Illume Labs, TrueWellness and more
One app with what these 6 AI tools do, yours to keep and change: Illume Labs, TrueWellness, SmolVLM2, Kim Personal Health Assistant, Insightfull, Gym Hero.
Everything these tools do, in one app
- Wearable data sync Automatically pulls sleep, activity, heart rate, and recovery data from connected wearables or health platforms.Found in Illume Labs, Kim Personal Health Assistant, Insightfull and 1 more
- Unified health profile Combines data from multiple apps, wearables, and labs into one place.Found in Illume Labs, TrueWellness, Kim Personal Health Assistant and 1 more
- Natural-language health questions Lets users ask free-form questions about their own health data and get answers.Found in Illume Labs, Kim Personal Health Assistant, Insightfull
- Cross-source pattern detection Finds correlations and patterns across different types of health data.Found in Illume Labs, Kim Personal Health Assistant, Insightfull
- Food and meal logging Records what you eat, including via meal photos, to track nutrition.Found in Illume Labs, Kim Personal Health Assistant, Insightfull
- Lab result upload Lets users upload bloodwork or lab results and connect those markers to lifestyle factors.Found in Illume Labs, TrueWellness
- Proactive insights Sends updates about what changed, what it might mean, and suggested next steps.Found in Illume Labs, TrueWellness
- Personal experiments Suggests simple tests to try on yourself to see how habits affect how you feel.Found in Kim Personal Health Assistant
- Context logging Adds subjective information like mood, energy, supplements, and habits to sensor data.Found in Kim Personal Health Assistant, Insightfull
- Symptom and medication logging Tracks symptoms and medications to help identify correlations.Found in Insightfull
- Automated wellness plans Creates personalized health plans and adherence protocols to turn insights into action.Found in TrueWellness
- Practitioner-style guidance Uses AI trained on experienced practitioners to give guidance at scale.Found in TrueWellness
- Personalized workout plans Generates tailored exercise plans based on user input and progress.Found in Gym Hero
- Exercise tracking and analysis Tracks workouts in detail and automatically analyzes performance.Found in Gym Hero
- Progress visualization Shows progress through charts and reports.Found in Gym Hero
- Community challenges Provides community support and motivation through shared challenges.Found in Gym Hero
- Multimodal image-text understanding Understands and generates content based on both images and text.Found in SmolVLM2
- Lightweight model deployment Runs efficiently on machines with limited hardware or edge devices.Found in SmolVLM2
How it works, step by step
- Sync sleep, activity, heart rate and recovery data from connected wearables
- Combine apps, wearables and labs into one health profile
- Answer free-form questions about the user's own data
- Detect correlations across different health data types
- Log meals, including from meal photos
- Upload bloodwork and connect markers to lifestyle factors
- Send proactive updates on what changed and possible next steps
- Suggest simple personal experiments on habits
- Log mood, energy, supplements and habits
- Track symptoms and medications for correlation
- Draft personalized wellness plans and adherence steps
- Provide practitioner-style guidance with source references
- Generate tailored workout plans from user input and progress
- Track workouts and analyze performance
- Visualize progress through charts and reports
- Run community challenges for shared motivation
- Read meal photos and notes together with text
- Run on limited hardware or edge devices
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Personal health evidence and reporting workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Personal health evidence and reporting workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare27 KB
- prompt-vps.mdThe same build on your own server (Docker)27 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria15 KB
- demo/index.htmlThe working demo on sample data198 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce the effort of turning scattered health data into reviewed, plain-language answers and plans. For individuals combining wearable, log and lab data who want plain-language answers and plans, convert connected wearable data, meal and habit logs, lab results and symptom notes into user-reviewed health summaries linked to source records. The benefit is a testable hypothesis, measured through accepted summaries per user month and corrections after review; do not assume that AI output alone produces business value.
Confirm the user's problem and scope, collect connected wearable data, meal and habit logs, lab results and symptom notes, then follow this sequence: 1. Sync sleep, activity, heart rate and recovery data from connected wearables. 2. Combine apps, wearables and labs into one health profile. 3. Answer free-form questions about the user's own data. Resolve uncertain cases with qualified reviewers, approve user-reviewed health summaries linked to source records, and measure accepted summaries per user month and corrections after review against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final medical interpretation and treatment decisions remain with qualified clinicians. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve user privacy, source attribution, data accuracy and usage permissions. Users approve substantive changes and sharing scope. One wearable platform and one lab upload format; final medical interpretation remains with qualified clinicians. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One wearable platform and one lab upload format; final medical interpretation remains with qualified clinicians. Implement one approved input format, a bounded representative case set and the first two task modules: sync sleep, activity, heart rate and recovery data from connected wearables; combine apps, wearables and labs into one health profile. Support the third module with operator review: answer free-form questions about the user's own data. Include source references, corrections, basic account access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
User-owned wearable accounts, lab portals and permitted health sources. Cloud record storage, health-file import/export and clinician sharing destinations. Start with file exchange and validate destination specifications before promising direct sharing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Data connections and profile, Editable health summary, Plan and progress view. Use a dashboard of connected sources, a large central summary canvas, and a right-hand panel for source records, flags and comments. Let users compare periods side by side. Display draft, changes requested and approved states. Provide a shareable clinician link with comments anchored to the relevant record. Make the task-specific outcome user-reviewed health summaries linked to source records visible beside its evidence, review state and value baseline.
