Background:　Publicly　accessible　critical　care-related　databases　contain　enormous　clinical　data,　but　their　utilization　often　requires　advanced　programming　skills.　The　growing　complexity　of　large　databases　and　unstructured　data　presents　challenges　for　clinicians　who　need　programming　or　data　analysis　expertise　to　utilize　these　systems　directly.　Objective:　This　study　aims　to　simplify　critical　care-related　database　deployment　and　extraction　via　large　language　models.　Methods:　The　development　of　this　platform　was　a　2-step　process.　First,　we　enabled　automated　database　deployment　using　Docker　container　technology,　with　incorporated　web-based　analytics　interfaces　Metabase　and　Superset.　Second,　we　developed　the　intensive　care　unit-generative　pretrained　transformer　(ICU-GPT),　a　large　language　model　fine-tuned　on　intensive　care　unit　(ICU)　data　that　integrated　LangChain　and　Microsoft　AutoGen.　Results:　The　automated　deployment　platform　was　designed　with　user-friendliness　in　mind,　enabling　clinicians　to　deploy　1　or　multiple　databases　in　local,　cloud,　or　remote　environments　without　the　need　for　manual　setup.　After　successfully　overcoming　GPT＇s　token　limit　and　supporting　multischema　data,　ICU-GPT　could　generate　Structured　Query　Language　(SQL)　queries　and　extract　insights　from　ICU　datasets　based　on　request　input.　A　front-end　user　interface　was　developed　for　clinicians　to　achieve　code-free　SQL　generation　on　the　web-based　client.　Conclusions:　By　harnessing　the　power　of　our　automated　deployment　platform　and　ICU-GPT　model,　clinicians　are　empowered　to　easily　visualize,　extract,　and　arrange　critical　care-related　databases　more　efficiently　and　flexibly　than　manual　methods.　Our　research　could　decrease　the　time　and　effort　spent　on　complex　bioinformatics　methods　and　advance　clinical　research.　JMIR　Med　Inform　2025;13:e63216;　doi:　10.2196/63216