Project ideas from Hacker News discussions.

I let ChatGPT analyze a decade of my Apple Watch data, then I called my doctor

📝 Discussion Summary (Click to expand)

Three dominant themes in the discussion

# Theme Key points & representative quotes
1 Consumer health data is noisy and AI can mis‑interpret it • “Apple says it collects an ‘estimate’ of VO₂ max … independent researchers found those estimates can run low – by an average of 13 percent.” – freedomben
• “Apple Watch underestimated VO₂ max, with a mean difference of 6.07 mL/kg/min … MAPE was 13.31 %.” – ignoramous
• “Health metrics are absolutely tarnished by a lack of proper context … you can’t reliably take a concept as broad as health and reduce it to a number.” – chrisfosterelli
2 AI health tools are marketed as trustworthy while being unreliable • “OpenAI is practically begging you to jump in and use it for personal, life or death type decisions, and does very little to help you understand when it may be wrong.” – anon7000
• “The product itself is telling you in plain English that it’s ABSOLUTELY CERTAIN about its answer… even when you challenge it.” – anon7000
• “The problem is that AI companies are selling, advertising, and shipping AI as a tool that works most of the time for what you ask it to do. That’s deeply misleading.” – anon7000
3 Human doctors still need to be the final arbiter; AI should be a supplement, not a replacement • “I would never let an LLM make an amputate or not decision, but it could convince me to go talk with an expert who sees me in person …” – maerF0x0
• “Good doctors will counsel you and tell you that the lab results are just one metric and one input.” – Shank
• “Without a proper clinical validation, they are not worth to try.” – sinuhe69

These three themes—data reliability, marketing misrepresentation, and the need for human oversight—capture the core concerns voiced by the majority of commenters.


🚀 Project Ideas

FitCheck: Wearable Data Validation & Confidence Dashboard

Summary

  • Provides statistical validation, confidence intervals, and outlier detection for wearable metrics (VO₂ max, HRV, etc.).
  • Flags device‑specific biases and offers trend‑based context to avoid misinterpretation.
  • Seamlessly exports validated data to EHRs or personal health records.

Details

Key Value
Target Audience Fitness enthusiasts, patients using wearables, clinicians
Core Feature Data validation engine, confidence scoring, trend analysis, EHR export
Tech Stack Python, Pandas, FastAPI, PostgreSQL, React, Docker
Difficulty Medium
Monetization Revenue‑ready: $9.99/month per user or $499/clinic

Notes

  • HN commenters lament “Apple says it collects an estimate… independent researchers found…”. FitCheck turns that estimate into a trustworthy metric.
  • Sparks discussion on cross‑device calibration and the trade‑off between convenience and accuracy.

HealthChat: Transparent AI Health Advisor

Summary

  • AI chatbot that explicitly reports uncertainty, cites sources, and refuses to give definitive medical advice unless qualified.
  • Provides a “confidence score” and links to peer‑reviewed literature for every claim.
  • Designed to mitigate hallucinations and build user trust.

Details

Key Value
Target Audience General public, health researchers, developers
Core Feature Uncertainty estimation, source citation, refusal logic, knowledge base
Tech Stack OpenAI GPT‑4, LangChain, Node.js, PostgreSQL, React
Difficulty Medium
Monetization Revenue‑ready: $0.02/query or $19.99/month subscription

Notes

  • Addresses the “ChatGPT is AI and can make mistakes” narrative and the marketing hype that “ChatGPT is the tool to use if you want to arrive at the truth.”
  • Opens debate on setting appropriate uncertainty thresholds and legal liability.

LabLens: Patient‑Centric Lab Result Interpreter

Summary

  • Upload raw lab results, receive contextualized explanations, personalized next‑step suggestions, and privacy‑controlled sharing.
  • Translates complex metrics (e.g., eGFR, BMI, VO₂ max) into lay language and actionable insights.
  • Protects data with HIPAA‑compliant storage and granular consent.

Details

Key Value
Target Audience Patients, caregivers, health‑tech hobbyists
Core Feature Lab result parser, context engine, explanation generator, next‑step suggestions, privacy controls
Tech Stack Python, FastAPI, SQLite, React Native, HIPAA‑compliant cloud storage
Difficulty Medium
Monetization Hobby (open source) or Revenue‑ready: $4.99/month for premium insights

Notes

  • Responds to frustration that “I don’t see how they are considered a health fad” and the confusion around lab metrics.
  • Encourages discussion on how to handle ambiguous results and avoid unnecessary testing.

DoctorSync: Integrated Health Data Platform

Summary

  • Secure platform for clinicians to ingest wearable data, lab results, and patient notes, with trend dashboards and red‑flag alerts.
  • Role‑based access ensures only authorized staff see sensitive data.
  • Integrates with existing EHRs via FHIR APIs.

Details

Key Value
Target Audience Primary care physicians, specialists, health‑system IT
Core Feature Data ingestion, EHR integration, trend dashboards, alert system, role‑based access
Tech Stack Java/Spring Boot, PostgreSQL, FHIR API, Angular, Docker
Difficulty High
Monetization Revenue‑ready: $1,200/physician/year license

Notes

  • Addresses the need for doctors to “interpret wearable data” and patients to “share data” without confusion.
  • Sparks conversation on interoperability standards and the cost of clinical validation.

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