AI systems become useful when they produce evidence people can inspect, test, and use.
AI is becoming a horizontal layer for scientific evidence, workflow execution, software development, clinical interpretation, and product delivery. My work focuses on agentic systems where retrieval, structured extraction, code generation, clinical information handling, verification, and human review are built into the workflow from the start.
Scientific agents need structured sources, defined tasks, reviewable outputs, and clear links between input, reasoning step, evidence, and result.
AI-assisted clinical genetics requires secure deployment, controlled data flow, reproducible report generation, and human review by expert boards.
Systematic review, freedom-to-operate, and patent workflows need traceable extraction from article, claim, field, code, test, and human validation.
Agentic systems need credentialed access, clear user tasks, structured outputs, auditability, and product design that makes AI usable in real workflows.
Retrieval, task planning, evidence review, structured extraction, code generation, output comparison, failure inspection, and iterative refinement. Experienced with OpenAI ChatGPT, Google Gemini, Anthropic Claude, Hugging Face.
Retrieval-augmented workflows, embeddings, literature search, mechanistic text summarisation, document chunking, metadata mapping, and source-linked outputs.
Variant interpretation support, report drafting, ICD-10 conversion, candidate diagnosis generation, Molecular Board review, and clinical information structuring.
Open-weight models, Llama, DeepSeek R1, BioMedIT infrastructure, secure high-performance computing, controlled access, and review-ready outputs.
Claim extraction, source tracking, structured reports, manual sampling, validation, audit trails, reproducible code, and reviewable uncertainty.
Customer-facing applications, credentialed users, OpenAI APIs, interface design, workflow design, documentation, stakeholder feedback, and operational rollout.
Built an AI-assisted information layer over structured disease-gene panel evidence, combining UniProtKB mechanistic text with panel metadata, inheritance, disease group, and reported phenotypes.
6.6 million features compressed 5x into disease-aware summaries, integrated into a user-facing application for interpretation and review.
Built a secure AI-assisted clinical genetics system on BioMedIT high-performance computing infrastructure using open-weight models including Llama and DeepSeek R1.
High-throughput variant interpretation and report generation for human review by the Molecular Board, including geneticists, doctors, and researchers.
Developed a credentialed platform using OpenAI APIs and agent workflows to collate clinical information and convert ICD-10 codes into candidate diagnostic hypotheses.
Customer-facing interface, controlled access, structured clinical inputs, candidate diagnosis generation, and reviewable outputs.
Built AI-assisted workflows for systematic review, structured extraction, algorithm development, freedom-to-operate, patent review, and trademark analysis.
More than 5,000 full-text research articles processed into structured datasets; evidence preparation for Switzerland Omics trademark registration with the Swiss Federal Institute of Intellectual Property.
PanelAppRex aggregates disease gene panels and facilitates sophisticated search. Quant Group, Simon Boutry, Ali Saadat, Sinisa Savic, Luregn J. Schlapbach, Jacques Fellay, Dylan Lawless. Bioinformatics Advances (2026). DOI: 10.1093/bioadv/vbag115.
Repository | medRxiv | PDF | Video | Application | About | Dataset
Reply to Dages et al: You AIn’t using it right: artificial intelligence progress in allergy. Dylan Lawless. Journal of Allergy and Clinical Immunology 153(1), 355 to 356, 2024. DOI: 10.1016/j.jaci.2023.09.023.
AI systems for science and medicine need traceable inputs, reviewable outputs, secure deployment, and evidence that can be tested. My work focuses on agentic systems that help teams move from documents, data, code, and clinical information into structured outputs that people can inspect, validate, and use.
