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Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions. A deep learning system able to identify the most common skin conditions may help clinicians in making more accurate diagnoses in routine clinical practice

\"Over 900 tests help you reach a diagnosis! The new fourth edition of Laboratory Tests and Diagnostic Procedures is the most complete reference of its kind. Part One consists of a unique alphabetical list of more than 600 diseases, conditions, and symptoms, paired with the tests and procedures commonly used to rule out or confirm each condition. Part Two represents an up-to-date, concise, complete compilation of virtually every laboratory and diagnostic test available to clinicians today.\"--Jacket.

Plasma tau phosphorylated at threonine 217 (p-tau217) and plasma tau phosphorylated at threonine 181 (p-tau181) are associated with Alzheimer's disease tau pathology. We compared the diagnostic value of both biomarkers in cognitively unimpaired participants and patients with a clinical diagnosis of mild cognitive impairment, Alzheimer's disease syndromes, or frontotemporal lobar degeneration (FTLD) syndromes. In this retrospective multicohort diagnostic performance study, we analysed plasma samples, obtained from patients aged 18–99 years old who had been diagnosed with Alzheimer's disease syndromes (Alzheimer's disease dementia, logopenic variant primary progressive aphasia, or posterior cortical atrophy), FTLD syndromes (corticobasal syndrome, progressive supranuclear palsy, behavioural variant frontotemporal dementia, non-fluent variant primary progressive aphasia, or semantic variant primary progressive aphasia), or mild cognitive impairment; the participants were from the University of California San Francisco (UCSF) Memory and Aging Center, San Francisco, CA, USA, and the Advancing Research and Treatment for Frontotemporal Lobar Degeneration Consortium (ARTFL; 17 sites in the USA and two in Canada). Participants from both cohorts were carefully characterised, including assessments of CSF p-tau181, amyloid-PET or tau-PET (or both), and clinical and cognitive evaluations. Plasma p-tau181 and p-tau217 were measured using electrochemiluminescence-based assays, which differed only in the biotinylated antibody epitope specificity. Receiver operating characteristic analyses were used to determine diagnostic accuracy of both plasma markers using clinical diagnosis, neuropathological findings, and amyloid-PET and tau-PET measures as gold standards. Difference between two area under the curve (AUC) analyses were tested with the Delong test. Data were collected from 593 participants (443 from UCSF and 150 from ARTFL, mean age 64 years [SD 13], 294 [50%] women) between July 1 and Nov 30, 2020. Plasma p-tau217 and p-tau181 were correlated (r=0·90, p<0·0001). Both p-tau217 and p-tau181 concentrations were increased in people with Alzheimer's disease syndromes (n=75, mean age 65 years [SD 10]) relative to cognitively unimpaired controls (n=118, mean age 61 years [SD 18]; AUC=0·98 [95% CI 0·95–1·00] for p-tau217, AUC=0·97 [0·94–0·99] for p-tau181; pdiff=0·31) and in pathology-confirmed Alzheimer's disease (n=15, mean age 73 years [SD 12]) versus pathologically confirmed FTLD (n=68, mean age 67 years [SD 8]; AUC=0·96 [0·92–1·00] for p-tau217, AUC=0·91 [0·82–1·00] for p-tau181; pdiff=0·22). P-tau217 outperformed p-tau181 in differentiating patients with Alzheimer's disease syndromes (n=75) from those with FTLD syndromes (n=274, mean age 67 years [SD 9]; AUC=0·93 [0·91–0·96] for p-tau217, AUC=0·91 [0·88–0·94] for p-tau181; pdiff=0·01). P-tau217 was a stronger indicator of amyloid-PET positivity (n=146, AUC=0·91 [0·88–0·94]) than was p-tau181 (n=214, AUC=0·89 [0·86–0·93]; pdiff=0·049). Tau-PET binding in the temporal cortex was more strongly associated with p-tau217 than p-tau181 (r=0·80 vs r=0·72; pdiff<0·0001, n=230). Both p-tau217 and p-tau181 had excellent diagnostic performance for differentiating patients with Alzheimer's disease syndromes from other neurodegenerative disorders. There was some evidence in favour of p-tau217 compared with p-tau181 for differential diagnosis of Alzheimer's disease syndromes versus FTLD syndromes, as an indication of amyloid-PET-positivity, and for stronger correlations with tau-PET signal. Pending replication in independent, diverse, and older cohorts, plasma p-tau217 and p-tau181 could be useful screening tools to identify individuals with underlying amyloid and Alzheimer's disease tau pathology. US National Institutes of Health, State of California Department of Health Services, Rainwater Charitable Foundation, Michael J Fox foundation, Association for Frontotemporal Degeneration, Alzheimer's Association.

A comprehensive differential diagnosis is a cornerstone of medical care that is often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by large language models present new opportunities to assist and automate aspects of this process 1 . Here we introduce the Articulate Medical Intelligence Explorer (AMIE), a large language model that is optimized for diagnostic reasoning, and evaluate its ability to generate a differential diagnosis alone or as an aid to clinicians. Twenty clinicians evaluated 302 challenging, real-world medical cases sourced from published case reports. Each case report was read by two clinicians, who were randomized to one of two assistive conditions: assistance from search engines and standard medical resources; or assistance from AMIE in addition to these tools. All clinicians provided a baseline, unassisted differential diagnosis prior to using the respective assistive tools. AMIE exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% versus 33.6%, P  = 0.04). Comparing the two assisted study arms, the differential diagnosis quality score was higher for clinicians assisted by AMIE (top-10 accuracy 51.7%) compared with clinicians without its assistance (36.1%; McNemar’s test: 45.7, P  < 0.01) and clinicians with search (44.4%; McNemar’s test: 4.75, P  = 0.03). Further, clinicians assisted by AMIE arrived at more comprehensive differential lists than those without assistance from AMIE. Our study suggests that AMIE has potential to improve clinicians’ diagnostic reasoning and accuracy in challenging cases, meriting further real-world evaluation for its ability to empower physicians and widen patients’ access to specialist-level expertise. Diagnostic reasoning using an optimized large language model with a dataset comprising real-world medical cases exhibited improved differential diagnostic performance as an assistive tool for clinicians over search engines and standard medical resources.

\"Improved diagnostic sophistication is increasingly enabling neurologists to differentiate between Parkinson's disease and other atypical parkinsonism (AP), such as multiple system atrophy, progressive supranuclear palsy, corticobasal degeneration, and dementia with Lewy bodies. The Handbook of Atypical Parkinsonism is a comprehensive survey of all diseases of this category, providing an authoritative guide to the recognition, diagnosis and management of these disorders. Each chapter follows a common structure, commencing with the full basic science of the disorder under consideration, followed by descriptions of the clinical picture and differential diagnosis. Subsequent chapters discuss current and future therapeutic approaches to these difficult conditions. Written and edited by leading practitioners in the field, clinicians in neurology and other specialties will find this book essential to the understanding and diagnosis of this complex group of disorders\"--Provided by publisher.

The ultimate companion volume to DSM-5-TR, the Study Guide is designed to help clinical learners, teachers, and practitioners in psychiatry, psychology, and social work understand and apply diagnostic criteria. Medical students, residents, and fellows and trainees throughout the clinical neurosciences will find this text to be invaluable in developing their diagnostic abilities. Readers learn key clinical content through concise chapters and can assess their knowledge with more than 100 multiple choice questions. The book is organized into three sections: • Part I delves into foundational concepts of psychiatric diagnoses and explores how diagnostic assessment fits into the process of working therapeutically with patients. In this new edition, Part I includes a new chapter examining structural and cultural considerations in arriving at a diagnosis.• Part II focuses on the diagnostic classes within DSM-5-TR and features clinical case vignettes with real-life complexity relevant to psychiatric diagnoses, diagnostic pearls that can be applied to daily clinical practice, and a self-assessment section with key concepts, questions for further discussion, patient cases, and Q&As.• Part III includes more than 100 multiple choice questions with an answer key. These questions cover a range of diagnoses and patient presentations from across DSM-5-TR, helping readers review their learning and assess their skills. Taking a patient-centered approach that complements the more disorder-centered organization of DSM-5-TR, this Study Guide provides context for DSM-5-TR's diagnoses while offering an array of interesting and memorable cases. Rich in thought-provoking detail and designed for repeated use, this is an indispensable guide for learners, educators, and clinicians.

Accurate diagnosis of multiple sclerosis requires careful attention to its differential diagnosis—many disorders can mimic the clinical manifestations and paraclinical findings of this disease. A collaborative effort, organised by The International Advisory Committee on Clinical Trials in Multiple Sclerosis in 2008, provided diagnostic approaches to multiple sclerosis and identified clinical and paraclinical findings (so-called red flags) suggestive of alternative diagnoses. Since then, knowledge of disorders in the differential diagnosis of multiple sclerosis has expanded substantially. For example, CNS inflammatory disorders that present with syndromes overlapping with multiple sclerosis can increasingly be distinguished from multiple sclerosis with the aid of specific clinical, MRI, and laboratory findings; studies of people misdiagnosed with multiple sclerosis have also provided insights into clinical presentations for which extra caution is warranted. Considering these data, an update to the recommended diagnostic approaches to common clinical presentations and key clinical and paraclinical red flags is warranted to inform the contemporary clinical evaluation of patients with suspected multiple sclerosis.