[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-460468-105":59,"doc-detail-460468-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","quantitative-semiology-harnessing-ai-generated-teaching-signals-in-psychiatry","Quantitative semiology - harnessing AI-generated teaching signals in psychiatry","","Quantitative semiology proposes a Bayesian framework for clinical sense-making in psychiatry, showing how clinicians combine prior beliefs with incoming narratives to form diagnostic inferences, and how teaching signals arise when new data reduce uncertainty and update expectations. It argues psychiatric practice often lacks precision due to fuzzy priors and vague, metaphorical reports. The article presents clinical signs as a missing data stream, observable at the bedside and measurable in physical or behavioral terms, enabling more precise incoming data that generate stronger teaching signals and improve clinical confidence.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/quantitative-semiology-harnessing-ai-generated-teaching-signals-in-psychiatry/460468/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/quantitative-semiology-harnessing-ai-generated-teaching-signals-in-psychiatry/460468.png","ImageObject",300,407,{"name":92,"@type":93},"Jiven","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the article describe clinical sense-making in medicine and psychiatry?","Question",{"text":112,"@type":113},"It frames clinical sense-making as a Bayesian process where prior beliefs are updated using incoming patient data to form diagnostic inferences.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why does the article say psychiatry often has less precision than other specialties?",{"text":117,"@type":113},"It attributes this to imprecise expectations about constructs and to incoming narratives being vague, metaphorical, or fragmented, which keeps inferences weak and open to criticism.",{"name":119,"@type":110,"acceptedAnswer":120},"What are “teaching signals” and how do they help clinicians?",{"text":121,"@type":113},"Teaching signals are mismatches between prior expectations and new incoming data that update beliefs, reduce uncertainty, and iteratively improve clinical confidence over repeated encounters.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},460468,1791937940,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":24,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399","OPEN ACCESS | Editorial  \nQuantitative semiology: harnessing AI-generated teaching signals in psychiatry  \nLena Palaniyappan a,b,c and Priyadharshini Sabesanb  \na Douglas Mental Health University Institute, Department of Psychiatry, McGill University, QC, Canada; bDepartment of Psychiatry, Schulich School of Medicine & Dentistry, University of Western Ontario London, ON, Canada; cRobarts Research Institute & Lawson Health Research Institute, London, ON, Canada  \nCorresponding author: Lena Palaniyappan (email: [lena.palaniyappan@mcgill.ca](lena.palaniyappan@mcgill.ca))  \n“Half of us are blind, few of us feel, and we are all deaf.” – William Osler  \nClinical sense-making in medicine and psychiatry  \nA clinician’s primary ordeal is deceptively simple: to make sense of another person’s suffering and offer solutions. This sense-making, the process of forming a coherent clinical understanding from data presented in various forms, is fundamentally a Bayesian process intuitive to clinicians1 (Fig. 1A) . We combine prior expectations (prior beliefs) with incoming data to infer the most likely illness labels (posterior beliefs) . For example, in a paediatric emergency where seeing a patient with acute abdominal pain is high (prior belief ), a child’s report of “pain started first around my belly button”(initial incoming data) is likely to invoke an inference of“appendicitis”(posterior belief ) but with some uncertainty. This uncertainty is greatly reduced by actively seeking clinical signs (rebound tenderness in right iliac fossa) and ultrasonogram (further evidence) . Every mismatch between our expectations and a new wave of incoming data serves to provide a “teaching signal” that updates our expectations and reduces the uncertainty of inferences (Fig. 1B) . This process, repeated over time, continuously improves the precision of a clinician’s beliefs. In short, teaching signals afford an iterative growth in clinical confidence with every encounter in medicine.2  \nPsychiatry lacks the precision of other medical specialties at multiple levels of this Bayesian sense-making process (Fig. 1C) . Many symptoms that we address are in continuum with normal human experience3 ; this means our expectations about target constructs (e.g., symptoms, diagnosis) are often imprecise or fuzzy. At the level of incoming data (i.e. , verbatim reports), we receive narratives that are often vague, metaphorical, or fragmented. With fuzzy priors and vague incoming data, the inferences (symptom labels and diagnostic entities) we make remain imprecise and open to criticism.4  \nHow, then, do we generate sufficient teaching signals that can improve our clinical sense-making? The JPN has a long tradition of supporting the quest for blood-based or brainbased biomarkers in psychiatry,5 which, when realised, could provide new waves of incoming data that aid the clinician. Nevertheless, while we wait for Godot, we can turn to a potential solution hidden in plain sight: clinical signs.  \nClinical signs are phenomena that are observable by looking, touching, or listening and can often be measured in physical terms at the proverbial bedside (e.g., the crackles or wheezes in asthma, the degree of abdominal distension in intestinal obstruction) . Some obvious examples in psychiatry include the pressured speech in mania, psychomotor slowness in depression, disorganised thinking in schizophrenia, stereotypical repetitions in autism, restless squirming in ADHD, and perseverative behaviors in dementia, to name a few.  \nClinical signs: the missing data stream  \nWhen trying to make sense of unclear narratives, we are left with three options: altering our prior beliefs, adding clarity to the vague incoming data (e.g., via rating scales), or looking for additional sources of data (i.e., clinical signs) . To understand why signs are our most viable path forward, it is helpful to first consider the alternatives.  \nThe humanistic tradition, led by Karl Jaspers, prescribesa direct manipul","cbCairQZRXBxO1Ez","https://ap.wps.com/l/cbCairQZRXBxO1Ez","pdf",397405,"English","# Clinical sense-making in medicine and psychiatry\n## Clinical signs: the missing data stream","[{\"question\":\"How does the article describe clinical sense-making in medicine and psychiatry?\",\"answer\":\"It frames clinical sense-making as a Bayesian process where prior beliefs are updated using incoming patient data to form diagnostic inferences.\"},{\"question\":\"Why does the article say psychiatry often has less precision than other specialties?\",\"answer\":\"It attributes this to imprecise expectations about constructs and to incoming narratives being vague, metaphorical, or fragmented, which keeps inferences weak and open to criticism.\"},{\"question\":\"What are “teaching signals” and how do they help clinicians?\",\"answer\":\"Teaching signals are mismatches between prior expectations and new incoming data that update beliefs, reduce uncertainty, and iteratively improve clinical confidence over repeated encounters.\"}]","Quantitative semiology - harnessing AI-generated teaching signals in psychiatry | PDF",1790758568,13]