Diagnosis最新文献

{"title":"The first Japanese conference on pediatric diagnostic excellence.","authors":"Toshiki Nakamura, Reo Uegaki, Takahiro Suzuki, Takahiro Matsushima, Takateru Ihara, Kenichi Tetsuhara, Katsunori Kamimura, Takashi Watari, Taro Shimizu","doi":"10.1515/dx-2026-0036","DOIUrl":"10.1515/dx-2026-0036","url":null,"abstract":"","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"146-149"},"PeriodicalIF":1.9,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147765473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Diagnostic Efficiency Ratio (DER): conceptual rationale, mathematical derivation, and hypothetical evaluation of a prevalence-sensitive benefit-harm metric.","authors":"Matthew L Rubinstein","doi":"10.1515/dx-2026-0001","DOIUrl":"https://doi.org/10.1515/dx-2026-0001","url":null,"abstract":"<p><strong>Objectives: </strong>Traditional diagnostic accuracy metrics - sensitivity, specificity, predictive values, likelihood ratios, and global indices such as Youden's J and the diagnostic odds ratio - describe important statistical properties of diagnostic tests but do not express benefit-harm balance in a single, prevalence-sensitive measure. The Diagnostic Efficiency Ratio (DER) is introduced as a composite metric designed to quantify diagnostic discriminative efficiency.</p><p><strong>Methods: </strong>DER is defined as the ratio of the number needed to misdiagnose (NNM) to the number needed to diagnose (NND), where NND=1/(<i>Sn</i> + <i>Sp</i> - 1) and NNM=1/[(1 - <i>Sp</i>) + <i>Pr</i> × (<i>Sp</i> - <i>Sn</i>)]. Closed-form derivation was performed analytically and verified using independent algebraic tools. Hypothetical scenarios spanning plausible ranges of sensitivity, specificity, and prevalence were constructed to illustrate DER behavior, and an empirical illustration using published neonatal sepsis biomarker studies was conducted.</p><p><strong>Results: </strong>Across hypothetical scenarios, DER demonstrated mathematically coherent and clinically intuitive behavior. High DER values occurred only when sensitivity and specificity were jointly strong and balanced. DER declined as specificity decreased or prevalence increased, with small values indicating contexts in which misdiagnoses consume a larger share of testing effort. In the empirical neonatal sepsis examples, DER differentiated biomarkers and settings in a manner consistent with its structural formulation, highlighting context-dependent diagnostic efficiency.</p><p><strong>Conclusions: </strong>DER provides a prevalence-sensitive, frequency-based representation of diagnostic benefit relative to harm, reframing diagnostic accuracy in efficiency-oriented terms by expressing how effectively testing effort is converted into discriminative gain beyond chance relative to misclassification burden. This conceptual and illustrative evaluation supports DER's coherence and potential stewardship utility as a complement to traditional diagnostic accuracy metrics. Ultimately, the DER helps to recast diagnostic performance as an efficiency problem, aligning it with modern value-based healthcare and stewardship priorities.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147728551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What is diagnosis in the age of artificial intelligence.","authors":"Lakshmi Krishnan, Christine Cassel","doi":"10.1515/dx-2025-0166","DOIUrl":"https://doi.org/10.1515/dx-2025-0166","url":null,"abstract":"<p><p>The integration of artificial intelligence into clinical practice demands fundamental reconsideration of how we conceptualize diagnosis. Clinical and popular discourse often frames diagnosis as a \"virtuoso model\" emphasizing individual physician expertise, which inadequately addresses the complexities of human-AI collaboration and overlooks work on its distributed and social dimensions. We propose a pluralistic framework moving beyond this paradigm toward a model better suited for AI integration. Drawing from medical and cultural history, sociological analysis, and cognitive science, we identify four key shifts necessary to reconceptualize diagnosis for the AI context: recognizing diagnosis as distributed cognition rather than simply individual virtuosity; embracing metacognitive awareness for human-AI collaboration; acknowledging patients as diagnostic co-creators rather than passive subjects; and understanding diagnosis as contextual and culturally situated. Successful AI integration requires moving beyond viewing AI as threat or efficiency tool toward reconceptualizing diagnosis as a collaborative, technologically-mediated activity where clinicians function as interpreters, patients as co-creators, and AI as specialized partners with transparent limitations. This framework demands new approaches to medical education and human-computer interaction research to realize AI's potential while preserving essential human judgment.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147716393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new perspective: blending ecological psychology and Safety II to learn from diagnostic excellence.","authors":"Jonathan G Sawicki, Russell Leslie, Daniel Nystrom","doi":"10.1515/dx-2025-0149","DOIUrl":"10.1515/dx-2025-0149","url":null,"abstract":"<p><strong>Objectives: </strong>Traditional safety frameworks applied in isolation have been insufficient to address the problem of diagnostic error in healthcare. We created a novel blend of approaches - leveraging Safety II and ecological psychology - to investigate diagnostic good catch events where healthcare teams made timely and accurate diagnoses.</p><p><strong>Methods: </strong>We performed a case series of qualitative interviews, grounded in ecological psychology, with individuals involved in diagnostic good catch events at our institution. From these interviews, we identified facilitators to the diagnostic process which were organized in a People Environments Tools and Technologies Scan.</p><p><strong>Results: </strong>We conducted 3 semi-structured interviews with a total of 4 clinicians. We identified numerous strategies, activities and contextual features that contributed to an individual's and team's ability to make accurate and timely diagnoses. Examples included the use of a \"trust but verify\" practice when admitting new patients, a thorough physical exam enhanced by point of care ultrasound, and the utility of open spaces to encourage collaboration among providers.</p><p><strong>Conclusions: </strong>A novel framework, coupling Safety II principles and ecological psychology, offers a complement to lessons learned from Safety I event reviews and provides opportunities to develop robust and pragmatic interventions to mitigate diagnostic error.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"505-509"},"PeriodicalIF":1.9,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147716388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of AI, diagnostic generator, and expert opinion in difficult neurologic diagnosis: real-world case study.","authors":"Pasquale F Finelli","doi":"10.1515/dx-2026-0028","DOIUrl":"https://doi.org/10.1515/dx-2026-0028","url":null,"abstract":"<p><strong>Objectives: </strong>Comparative diagnosis of AI vs. diagnostic generator and expert opinion in complex neurologic cases is limited. We present the results of a comparative study of these three diagnostic methods.</p><p><strong>Methods: </strong>A real-world study of two rare and challenging neurologic diseases was studied using 3 computer-assisted diagnostic programs, 2 AI (Chat-GPT4 and GLASS AI), diagnostic generator (NeurologicDx) and expert opinion.</p><p><strong>Results: </strong>The correct diagnosis was suggested by diagnostic generator but not by expert opinion or either AI program.</p><p><strong>Conclusions: </strong>Diagnostic generator outperformed AI and expert opinion supporting its use in difficult neurologic diagnosis.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147662324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic investigations: harmless tests or booby traps?","authors":"Glyn Elwyn, Laura Zwaan, Désirée Hairwassers, Paul K J Han","doi":"10.1515/dx-2025-0047","DOIUrl":"10.1515/dx-2025-0047","url":null,"abstract":"<p><strong>Introduction: </strong>Most people view diagnostic tests as harmless means of obtaining additional information and resolving uncertainties, thereby enabling the treatment of medical problems. Although this is true in most cases, there are situations in which it is not. The aim of the article is to demonstrate how diagnostic tests entail potential harms and benefits, arising from the knowledge they yield and its consequences, which has psychological, moral, and existential dimensions. These harms justify consideration of a strategy we call intentional unknowing, an choice to decline the acquisition of diagnostic knowledge. We show when and how this strategy is appropriate and propose a conceptual model that categorizes tests by the extent to which they provide high or low knowledge benefit and high or low knowledge harm.</p><p><strong>Observations: </strong>The need for shared decision-making is lowest when the potential benefits of diagnostic knowledge are low, and the potential harms are high. When the benefits and harms of diagnostic knowledge are both high, the need for shared decision-making is highest; the default position is collaborative deliberation. When the benefit of diagnostic knowledge is high and the harm is low, the need for shared decision-making depends on the test's accuracy. The model also illustrates when patients might prefer \"not to know\".</p><p><strong>Conclusions: </strong>As diagnostic and prognostic capabilities expand, uncertainty also increases, with significant implications for the utility of diagnostic knowledge and, in some situations, for ensuring that patients are informed about the balance of harms and benefits and that their preferences are respected.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"390-397"},"PeriodicalIF":1.9,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13110841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147644337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining atypical presentations associated with a higher risk of diagnostic errors using a new classification model.","authors":"Yukinori Harada, Tetsu Sakamoto, Yuya Wada, Kosuke Ishizuka, Junko Kubosaki, Koya Akutagawa, Tomoharu Suzuki, Sho Isoda, Toshinori Nishizawa, Taiju Miyagami, Satoshi Watanuki, Kotaro Kunitomo, Takashi Watari, Taro Shimizu","doi":"10.1515/dx-2025-0179","DOIUrl":"10.1515/dx-2025-0179","url":null,"abstract":"<p><strong>Objectives: </strong>To apply a new classification model for clinical presentations and detect a new subtype of atypical presentations associated with a higher risk of diagnostic errors, using a dataset of diagnostic errors collected with disease-agnostic approaches.</p><p><strong>Methods: </strong>We classified cases of diagnostic errors from a previous systematic review of case reports using a new classification model for clinical presentations. Based on a previous study, we defined four clinical presentation patterns as atypical presentations associated with a higher risk of diagnostic errors (atypical group). We described the characteristics of the atypical group, including codes from the Diagnostic Error Evaluation and Research (DEER) and Reliable Diagnosis Challenges (RDC) taxonomies. We compared them with those of the most typical presentation (typical group).</p><p><strong>Results: </strong>We included 382 cases in this study. The atypical groups accounted for 36 (9.4 %). For the RDC taxonomy, the codes \"Atypical presentations\", \"Findings masking/mimicking another diagnosis\", \"Equivocal results/interpretation\", and \"Recognizing misdiagnosis occurrence\" were assigned more frequently in the atypical group. There was no difference in the distribution of DEER taxonomy codes between the two groups.</p><p><strong>Conclusions: </strong>We detected atypical presentations associated with a higher risk of diagnostic errors by using the new model. Cases with such atypical presentations had more specific diagnostic challenges than those with the most typical presentation. The new model facilitates future studies to identify and address the target to reduce diagnostic errors in patients with atypical presentations associated with a higher risk of diagnostic errors.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"451-459"},"PeriodicalIF":1.9,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147644324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential missed diagnostic opportunities after low dose CT screening for lung cancer: results from the National Lung Cancer Screening Trial.","authors":"Ahmed Hassoon, Sarah Hussain, Panagis Galiatsatos, Harold Lehmann, Kathryn McDonald, Christine Berg, Otis Brawley","doi":"10.1515/dx-2025-0074","DOIUrl":"10.1515/dx-2025-0074","url":null,"abstract":"<p><strong>Objectives: </strong>Low-dose CT (LDCT) screening is critical for early lung cancer detection. We assessed diagnostic uncertainty following positive LDCTs in the National Lung Screening Trial (NLST). We defined this pathway as an \"Potential missed diagnostic opportunity\" for participants eventually diagnosed with lung cancer.</p><p><strong>Methods: </strong>This is a secondary analysis of the NLST trial, examining potential missed diagnostic opportunity among screen-detected participants who were diagnosed with lung cancer in the LDCT arm. <i>Potential missed diagnostic opportunity (PMDO)</i> defined as no lung cancer diagnosis following a positive LDCT, which was then followed by a subsequent annual positive LDCT that did lead to lung cancer diagnosis.</p><p><strong>Results: </strong>Of 1,089 lung cancers diagnosed in the LDCT arm, 58 % (630 individuals) were adherent screen-detected at risk for a <i>PMDO</i> (605 [55 %] using Lung-RADS). Overall, 37.3 % (235 of 630) experienced ≥1 PMDO (33.1 % using Lung-RADS). Among the 235 participants who experienced a <i>PMDO</i>, only 1.7 % (four individuals) received a diagnostic resection or biopsy, and 35.7 % (84 individuals) had little to no diagnostic procedures following their first positive LDCT. Once diagnosed, the group that experienced <i>PMDO</i> tended to have earlier stage lung cancer and longer survival rates compared to those without such events.</p><p><strong>Conclusions: </strong>While LDCT is a vital screening tool, a substantial proportion of lung cancer patients experience a period of diagnostic uncertainty after an initial positive screen. This pathway highlights a critical area for process improvement. Understanding the clinical decision-making during these periods is essential to fully optimize the benefits of early detection.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"430-438"},"PeriodicalIF":1.9,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147484730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive biases and collapse of prioritization accuracy under incongruent clinical data: a mixed-methods study of nursing diagnostic reasoning.","authors":"Alessandra Milani, Luisa Saiani, Sara Gandini, Eliana Misurelli, Silvana Lacapra, Claudia Cafiero, Sabrina Boselli, Maria Angela Massaro, Anna Sponton, Laura Zoppini, Ketti Mazzocco","doi":"10.1515/dx-2025-0178","DOIUrl":"10.1515/dx-2025-0178","url":null,"abstract":"<p><strong>Objectives: </strong>To characterize clinical reasoning in prioritization and test whether errors are linked to experience or are universal, by examining how information congruence and informativeness influence nurses' prioritization and diagnostic reasoning, and by identifying cognitive mechanisms underlying systematic errors under clinical uncertainty.</p><p><strong>Methods: </strong>A concurrent embedded mixed-methods study was conducted with 130 nurses from two university hospitals. Using a think-aloud protocol, participants reasoned through four experimentally controlled clinical scenarios in which information congruence (data aligned vs. misaligned with the most common diagnosis) and informativeness (amount of data) were manipulated. Prioritization accuracy (correct vs. incorrect priority) was the primary outcome. Qualitative analysis identified cognitive biases, which were entered into a logistic regression model to quantify their association with accuracy.</p><p><strong>Results: </strong>Accuracy collapsed when nurses faced incongruent clinical data, falling from 49.3 % in congruent scenarios to 18.4 % in incongruent ones (31-point drop; 95 % CI 20-42 %; p<0.001). This decrement was independent of age, experience, educational level, and ward type. Qualitative analysis showed that most nurses (71.4 %) actively dismissed critical conflicting cues. Confirmation bias (OR=0.048, p=0.015) and information bias (OR=0.082, p=0.010), were strong significant predictors of incorrect prioritization.</p><p><strong>Conclusions: </strong>Nursing prioritization errors are systematic cognitive failures rather than random mistakes or simple knowledge deficits. The core vulnerability appears to be metacognitive: an impaired ability to detect and resolve conflict between an activated mental model and new, incongruent information. Interventions to reduce diagnostic and prioritization errors should explicitly train cognitive and metacognitive skills for managing incongruence and flexibly updating clinical representations.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"439-450"},"PeriodicalIF":1.9,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147456084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic errors in abdominal pain: a secondary analysis of case reports.","authors":"Taku Harada, Yukinori Harada, Kotaro Kunitomo, Takashi Watari, Taro Shimizu","doi":"10.1515/dx-2025-0185","DOIUrl":"10.1515/dx-2025-0185","url":null,"abstract":"<p><strong>Objectives: </strong>Diagnostic errors remain common in abdominal pain, a frequent yet challenging presentation. We sought to compare diagnostic error profiles between abdominal and non-abdominal pain and to identify abdominal pain-specific, potentially modifiable targets in physical examination, diagnostic testing, and differential diagnosis.</p><p><strong>Methods: </strong>We conducted a retrospective secondary analysis of a systematically coded database of diagnostic error case reports. Adult pain cases were categorized as abdominal or non-abdominal after excluding pediatric and non-pain cases. Errors were classified using the Diagnostic Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC), and Generic Diagnostic Pitfalls (GDP) taxonomies. Frequencies of codes and contributing factors were compared.</p><p><strong>Results: </strong>Of 163 cases, 55 involved abdominal pain and 108 non-abdominal pain. The abdominal pain group had more RDC codes per case (3.1 ± 1.8 vs. 2.5 ± 1.7; p=0.032). Misinterpretation of physical findings and diagnostic tests, particularly imaging, was more common. Presentations with non-specific features or findings that masked/mimicked other conditions were also more frequent. Most physical examination errors (78 %) and imaging errors (82 %) reflected misinterpretation rather than underordering.</p><p><strong>Conclusions: </strong>Diagnostic errors in abdominal pain often involve misinterpreting physical and imaging findings and difficulty distinguishing among diseases within the correct organ system. Strengthening bedside diagnostic skills, fostering structured comparison of same-organ differential diagnoses, and ensuring reliable radiology support may reduce these errors and enhance patient safety.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"207-215"},"PeriodicalIF":1.9,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147431469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}