--- title: AI & RAG Consulting for Healthcare and Life Sciences source: https://rubradigital.com/industries/healthcare-life-sciences site: Rubra Digital description: Retrieval and LLM systems for pharma, medtech and healthcare providers, built for GxP expectations, HIPAA, EU MDR and the evidence these sectors require. updated: 2026-07-12 regulations: EU AI Act, EU MDR / IVDR, GDPR, HIPAA, GxP / GAMP 5, FDA software guidance, EMA guidance --- # AI & RAG Consulting for Healthcare and Life Sciences **Summary:** Rubra builds LLM and retrieval systems for pharmaceutical, medtech and healthcare organisations: regulatory document search, safety literature review, clinical documentation support and medical information response, designed around validation, traceability and human oversight. ## Where I see this working - **Regulatory dossier navigation.** Retrieval across submissions, variations and health authority correspondence spanning decades and dozens of markets, where the answer usually exists but finding it takes days. - **Safety literature screening.** First-pass triage of published literature for pharmacovigilance, with every decision traceable and a human reviewer on everything flagged. - **Medical information response drafting.** Draft responses to healthcare professional enquiries grounded strictly in approved labelling and the medical information library, never in model recollection. - **Clinical protocol and SOP assistance.** Site staff finding the applicable procedure across protocol amendments, with the version and effective date attached to every answer. - **Quality and deviation investigation support.** Retrieval across prior deviations, CAPAs and batch records to surface comparable historical cases during an investigation. Life sciences organisations have a specific and unusual advantage in this work: they already know how to validate a computerised system. The vocabulary of intended use, risk assessment, acceptance criteria and change control is established, and the quality function knows how to apply it. The adjustment is that language models are not deterministic, so acceptance criteria have to be expressed as measured performance on a fixed validation set rather than as exact-output tests. In my experience quality functions accept this readily once it is framed in terms they already use. The conversation goes badly only when engineering presents it as a new category of thing that existing frameworks cannot cover. ## Where I start Almost never with anything clinical. The fastest payback and lowest risk sit in regulatory and quality operations: dossier navigation, SOP retrieval, deviation investigation support. These are document-heavy, currently slow, and a wrong answer is caught by a human who was going to read the source anyway. Clinical use cases come later, if at all, and with the device question resolved first. ## Traceability is the deliverable In this sector the audit trail is not a supporting artefact. It is a large part of what you are buying. Every answer records which documents were retrieved, which versions of them, who asked, and what the system returned. That record is what makes the system defensible in an inspection, and it is designed in from the first sprint rather than added when someone asks for it. ## Frequently asked questions ### Can LLM systems be used in a GxP environment? Yes, within a validated scope and with the system positioned as decision support rather than as a decision maker. The practical approach is to treat it as computerised system validation under GAMP 5: documented intended use, risk assessment, defined and tested acceptance criteria, change control, and audit trails. The non-determinism of language models means acceptance criteria are stated statistically, as a measured accuracy threshold on a fixed validation set, rather than as exact-output tests. That framing needs to be agreed with quality assurance early. ### How do you handle protected health information? Preferably by not sending it anywhere. I de-identify before retrieval where the use case allows, enforce access control at retrieval time, and where PHI must be processed, use HIPAA-eligible services under a business associate agreement with in-region inference. For EU clients handling special category health data under Article 9 GDPR, EU-region inference and a documented Article 9 condition are the baseline. ### Is a clinical decision support tool a medical device? It may well be, under EU MDR or the equivalent FDA framework, and that determination changes the project entirely. The distinction usually turns on whether the software informs a clinical decision about an individual patient, and whether a clinician can independently review the basis for its output. This is a regulatory determination your regulatory affairs team must own, and it needs to be made before design starts, because a device pathway imposes requirements that cannot be retrofitted. --- Source: https://rubradigital.com/industries/healthcare-life-sciences Rubra Digital. Independent LLM and RAG consulting for regulated and document-heavy organisations in Europe and North America. Contact: hello@rubradigital.com