{"slug":"ai-medicine","topic":"ai-medicine","title":"AI in Clinical Medicine: State of the Field (October 2026)","summary":"Where AI for clinical medicine stands in October 2026: validated imaging models, pathology foundation models, large language models tested in real clinics, and AI-discovered drug candidates entering randomized trials. The main takeaway is that retrospective accuracy is common, prospective and real-world evidence is still thin, and benchmark scores need checking against real clinical use.","version":1,"published_at":"2026-10-30","updated_at":"2026-10-30","stats":{"words":2989,"citations":26,"claims":28,"timeline":15,"benchmarks":2,"open_problems":9,"key_papers":15,"glossary":13},"verification":{"status":"verified","method":"automated figure and citation-relevance triage against cited abstracts, plus targeted AI review with web checks of flagged statements; automated citation resolution","claims_checked":12,"issues_fixed":4,"verified_at":"2026-10-30"},"ai_assisted":true,"price":{"usd":"0.20","protocol":"x402","network":"eip155:84532","asset":"USDC"},"url":"https://space-kitty.com/dossiers/ai-medicine","preview_url":"https://space-kitty.com/api/v1/dossiers/ai-medicine/preview","purchase_url":"https://space-kitty.com/api/v1/dossiers/ai-medicine","toc":[{"level":2,"text":"Executive summary","anchor":"executive-summary"},{"level":2,"text":"Background and core concepts","anchor":"background-and-core-concepts"},{"level":2,"text":"Timeline of key milestones","anchor":"timeline-of-key-milestones"},{"level":2,"text":"State of the art","anchor":"state-of-the-art"},{"level":3,"text":"Imaging diagnosis","anchor":"imaging-diagnosis"},{"level":3,"text":"Digital pathology and foundation models","anchor":"digital-pathology-and-foundation-models"},{"level":3,"text":"Large language models in clinical text and decision support","anchor":"large-language-models-in-clinical-text-and-decision-support"},{"level":3,"text":"AI-discovered drug candidates","anchor":"ai-discovered-drug-candidates"},{"level":3,"text":"Quantitative benchmarks","anchor":"quantitative-benchmarks"},{"level":2,"text":"Open problems and active debates","anchor":"open-problems-and-active-debates"},{"level":2,"text":"Major programs and players","anchor":"major-programs-and-players"},{"level":2,"text":"Outlook: what to watch in the near term","anchor":"outlook-what-to-watch-in-the-near-term"},{"level":2,"text":"Key papers","anchor":"key-papers"},{"level":2,"text":"Glossary","anchor":"glossary"}],"sample_claims":["A deep network for chest radiographs was compared retrospectively with practicing radiologists and matched them on some findings but not all.","A knee MRI deep learning system was developed and validated retrospectively, with diagnostic variability and interpretation time as the motivating problems."],"includes":["markdown","claims","timeline","benchmarks","open_problems","key_papers","glossary","bibliography"],"citation_count":26,"how_to_buy":"GET https://space-kitty.com/api/v1/dossiers/ai-medicine with an x402 client ($0.20, USDC on Base Sepolia (testnet))."}