Project ideas from Hacker News discussions.

ChatGPT Health fails to recognise medical emergencies – study

📝 Discussion Summary (Click to expand)

Three dominant themes in the discussion

# Theme Key points & representative quotes
1 AI’s reliability and safety in medical contexts • “I have found the LLMs to be wrong in random insidious ways, so trusting them with anything critical is terrifying.” – steveBK123
• “It continues to amaze me how recklessly some people cram AI into spaces where it performs poorly and the consequences include death.” – josefritzishere
• “Even though these tools are showing time and time again that they have serious reliability issues, somehow people still think it is a good idea to use them for critical decisions.” – nerdjon
2 Human doctors vs. AI – a mixed‑signal comparison • “Doctors also miss things.” – WalterBright
• “I think the average Joe would assume these values were correct and run with it.” – y-c-o-m-b
• “Amazing how you can just deflect any criticism of LLMs here by going ‘but humans suck too!’” – emp17344
• “I’m not so sure. Doctors are trained to check for the most common things that explain the symptoms.” – SoftTalker
3 Need for rigorous testing, trials, and regulation • “We absolutely HAVE to go through the existing ruleset of conducting years of research and trials and approvals before pushing anything out to patients.” – hayleox
• “It would need to be tested. If doctors get lazy, complacent, or overworked, a ‘doctor with access to ChatGPT Health’ may be functionally equivalent to ‘just ChatGPT Health’.” – nerevarthelame
• “The study was feeding the AI structured clinical scenarios… not a live analysis of AI being used in the field.” – WarmWash

These three threads—concerns about AI’s accuracy, the ongoing debate over whether AI can or should replace human clinicians, and the call for formal, evidence‑based validation—capture the core of the conversation.


🚀 Project Ideas

Generating project ideas…

AI Verification Hub

Summary

  • Provides automated verification of AI-generated content (text, code, medical advice, devops commands) against trusted sources.
  • Flags hallucinations, supplies source citations, and assigns confidence scores to increase trust and compliance.
  • Enables audit trails for regulated domains like healthcare and insurance.

Details

Key Value
Target Audience Developers, doctors, insurers, compliance teams
Core Feature Verification engine that cross‑checks LLM outputs with curated knowledge bases and web sources, generates evidence links, and flags inconsistencies
Tech Stack Python, LangChain, OpenAI API, Pinecone vector DB, Scrapy for source extraction, PostgreSQL for audit logs
Difficulty High
Monetization Revenue‑ready: $49/user/month

Notes

  • HN users like “I treat the output with a much greater deal of skepticism” (y-c-o-m-b) would appreciate a tool that automatically surfaces evidence.
  • The platform can spark discussion on “AI hallucinations in devops” and “medical AI reliability” by providing transparent source traces.

SafeDevOps AI Executor

Summary

  • Wraps LLM‑generated devops commands in a safety layer that simulates, audits, and requires explicit human confirmation before execution.
  • Prevents accidental production changes and provides rollback capabilities.
  • Generates detailed audit logs for compliance and incident response.

Details

Key Value
Target Audience DevOps teams, SREs, cloud ops engineers
Core Feature LLM command interpreter → simulation engine → confirmation prompt → execution with rollback hooks
Tech Stack Go, Terraform, OpenAI API, Slack/Teams integration, PostgreSQL for logs
Difficulty Medium
Monetization Revenue‑ready: $99/team/month

Notes

  • Addresses the “AI accidentally restarted prod” scenario (rsynnott) and the “AI with dangerous access” concern (steveBK123).
  • Provides a concrete solution for the “AI in critical ops” pain point that many HN commenters highlight.

Medical Second Opinion AI with Evidence

Summary

  • Delivers AI‑generated medical diagnoses and treatment plans, each accompanied by evidence links, confidence scores, and an optional human review step.
  • Designed for patients seeking affordable second opinions and for doctors needing quick, evidence‑backed support.
  • Maintains HIPAA‑compliant data handling and auditability.

Details

Key Value
Target Audience Patients, primary care physicians, telehealth providers
Core Feature AI diagnosis engine → evidence retrieval from PubMed/clinical guidelines → confidence scoring → optional human review
Tech Stack Python, FastAPI, OpenAI API, PubMed API, Docker, HIPAA‑compliant storage
Difficulty High
Monetization Revenue‑ready: $29/consultation or $199/month for unlimited use

Notes

  • Responds to user frustration that “ChatGPT medical advice can be wrong” (SoftTalker, unstyledcontent) by providing verifiable sources.
  • Encourages discussion on “AI as a second opinion” versus “AI as a primary decision maker” and the need for evidence transparency.

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