--- title: Trustworthy AI in Healthcare and Longevity | daily.dev description: A TalkPython podcast episode featuring Sumit Gundawar, a software engineer building a clinical AI platform for a UK longevity clinic. The central argument is... canonical: https://daily.dev/posts/trustworthy-ai-in-healthcare-and-longevity-y31ti9qsv twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Trustworthy AI in Healthcare and Longevity | daily.dev og:description: A TalkPython podcast episode featuring Sumit Gundawar, a software engineer building a clinical AI platform for a UK longevity clinic. The central argument is... og:url: https://daily.dev/posts/trustworthy-ai-in-healthcare-and-longevity-y31ti9qsv og:image: https://api.daily.dev/og/posts/Y31tI9QSV.png og:image:alt: Trustworthy AI in Healthcare and Longevity og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Trustworthy AI in Healthcare and Longevity **[Planet Python](https://daily.dev/sources/planetpython)** · [@mkennedy](https://daily.dev/mkennedy) · 26 min read · 1 upvotes · 0 comments ## Summary A TalkPython podcast episode featuring Sumit Gundawar, a software engineer building a clinical AI platform for a UK longevity clinic. The central argument is that in high-stakes medical AI, the model itself is the easy part — earning trust through engineering is the real challenge. Key topics include RAG-based grounding that forces answers to cite sources, deterministic dosage checks instead of LLM-as-a-judge, prompt injection guards, PII redaction, Pydantic-validated structured outputs, and mandatory human-in-the-loop design required by the EU AI Act. The episode also covers local vs. frontier models for healthcare, the risk of model collapse from AI-generated training data, adversarial cross-model review, and futuristic full-body scanning ventures like Midjourney Medical and Neko Health. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do you stop an LLM from hallucinating a medical dosage that isn't in the source documents? Use a deterministic check rather than another LLM to grade the answer, since models are built to agree with you and studies show an LLM judge tends to favor the longest or first-listed answer regardless of correctness. A 'dosage card' approach instead checks a concrete claim like a milligram value against the exact text in retrieved documents using keyword and similarity matching, rejecting and routing to human review if the value isn't present in the sources. _Developers hardening AI-assisted medical or safety-critical tools can find more grounding and guardrail patterns like this on daily.dev._ ### Why might a RAG pipeline limit retrieved sources to a small number instead of pulling in as many documents as possible? Keeping retrieved sources to roughly four to seven avoids flooding the context window with weakly related passages, which would dilute the grounding check that measures how relevant retrieved context actually is to a question. A separate source-coverage and grounding-threshold check then decides whether the material sufficiently supports an answer before it's returned, rejecting and escalating to a human if not. _Anyone tuning RAG pipelines for reliability can track practical grounding tradeoffs like this via daily.dev._ ### Why does the EU AI Act require a human in the loop for medical AI systems? The EU AI Act classifies medical AI as very high risk, which legally mandates that a human review the decision rather than letting AI act alone, and also requires a full audit trail logging every accepted or rejected answer plus documents retrieved but found unrelated. This pushes system architecture toward transparency and traceability alongside US HIPAA restrictions on sharing medical data. _Teams navigating compliance-driven AI architecture can follow regulatory-shaped engineering patterns like this on daily.dev._ ## Similar posts on daily.dev - [How to stay curious as a dev in the AI hype era with Sumit Saha \[Podcast \#205\]](https://daily.dev/posts/how-to-stay-curious-as-a-dev-in-the-ai-hype-era-with-sumit-saha-podcast-205--7yxm1v9vt) · freeCodeCamp · 3 upvotes · 0 comments - [Weekly Dose \#4 - From Smarter Models to Safer Systems](https://daily.dev/posts/weekly-dose-4---from-smarter-models-to-safer-systems-yqez1xpcq) · Machine Learning Pills · 1 upvotes · 0 comments - [Agentic AI in Clinical Trials: The Real Challenge Isn't Automation — It's Trust • Arango](https://daily.dev/posts/agentic-ai-in-clinical-trials-the-real-challenge-isn-t-automation-it-s-trust-arango-krewfy5xp) · ArangoDB · 1 upvotes · 0 comments - [Balancing AI and Software Security with Mark Russinovich](https://daily.dev/posts/balancing-ai-and-software-security-with-mark-russinovich-shdgttplb) · OpenSSF · 0 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#rag](https://daily.dev/tags/rag), [#pydantic](https://daily.dev/tags/pydantic) [View this post on daily.dev](https://daily.dev/posts/trustworthy-ai-in-healthcare-and-longevity-y31ti9qsv) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Trustworthy AI in Healthcare and Longevity","url":"https://daily.dev/posts/trustworthy-ai-in-healthcare-and-longevity-y31ti9qsv","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/trustworthy-ai-in-healthcare-and-longevity-y31ti9qsv"},"datePublished":"2026-07-10T07:49:40.136Z","dateModified":"2026-09-14T08:00:22.869Z","description":"A TalkPython podcast episode featuring Sumit Gundawar, a software engineer building a clinical AI platform for a UK longevity clinic. The central argument is...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/7b8eec6acd624217a9e48b27244e5bc5?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/7b8eec6acd624217a9e48b27244e5bc5?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Planet Python","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Person","name":"Michael Kennedy","url":"https://daily.dev/mkennedy","image":"https://media.daily.dev/image/upload/s--CAIUuwxP--/f_auto,q_auto/v1701901463/avatars/avatar_5Q1yteeM08cxesQG2actL","description":"Python enthusiast. Host of @talkpython and @pythonbytes podcasts, founder of Talk Python Training.","interactionStatistic":{"@type":"InteractionCounter","interactionType":{"@type":"EndorseAction"},"userInteractionCount":30}},"commentCount":0,"discussionUrl":"https://daily.dev/posts/trustworthy-ai-in-healthcare-and-longevity-y31ti9qsv","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"python,rag,pydantic","timeRequired":"PT26M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Planet Python","item":"https://daily.dev/sources/planetpython"},{"@type":"ListItem","position":3,"name":"Trustworthy AI in Healthcare and Longevity"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/trustworthy-ai-in-healthcare-and-longevity-y31ti9qsv#faq","mainEntity":[{"@type":"Question","name":"How do you stop an LLM from hallucinating a medical dosage that isn't in the source documents?","acceptedAnswer":{"@type":"Answer","text":"Use a deterministic check rather than another LLM to grade the answer, since models are built to agree with you and studies show an LLM judge tends to favor the longest or first-listed answer regardless of correctness. A 'dosage card' approach instead checks a concrete claim like a milligram value against the exact text in retrieved documents using keyword and similarity matching, rejecting and routing to human review if the value isn't present in the sources. Developers hardening AI-assisted medical or safety-critical tools can find more grounding and guardrail patterns like this on daily.dev."}},{"@type":"Question","name":"Why might a RAG pipeline limit retrieved sources to a small number instead of pulling in as many documents as possible?","acceptedAnswer":{"@type":"Answer","text":"Keeping retrieved sources to roughly four to seven avoids flooding the context window with weakly related passages, which would dilute the grounding check that measures how relevant retrieved context actually is to a question. A separate source-coverage and grounding-threshold check then decides whether the material sufficiently supports an answer before it's returned, rejecting and escalating to a human if not. Anyone tuning RAG pipelines for reliability can track practical grounding tradeoffs like this via daily.dev."}},{"@type":"Question","name":"Why does the EU AI Act require a human in the loop for medical AI systems?","acceptedAnswer":{"@type":"Answer","text":"The EU AI Act classifies medical AI as very high risk, which legally mandates that a human review the decision rather than letting AI act alone, and also requires a full audit trail logging every accepted or rejected answer plus documents retrieved but found unrelated. This pushes system architecture toward transparency and traceability alongside US HIPAA restrictions on sharing medical data. Teams navigating compliance-driven AI architecture can follow regulatory-shaped engineering patterns like this on daily.dev."}}]} ```