Misdiagnosis of Multiple Sclerosis: Past, Present, and Future.

PURPOSE OF REVIEW: Misdiagnosis of multiple sclerosis (MS) is a prevalent worldwide problem. This review discusses how MS misdiagnosis has evolved over time and focuses on contemporary challenges and potential strategies for its prevention. RECENT FINDINGS: Recent studies report cohorts with a range of misdiagnosis between 5 and 18%. Common disorders are frequently misdiagnosed as MS. Overreliance on MRI findings and misapplication of MS diagnostic criteria are often associated with misdiagnosis. Emerging imaging biomarkers, including the central vein sign and paramagnetic rim lesions, may aid diagnostic accuracy when evaluating patients for suspected MS. MS misdiagnosis can have harmful consequences for patients and healthcare systems. Further research is needed to better understand its causes. Concerted and novel educational efforts to ensure accurate and widespread implementation of MS diagnostic criteria remain an unmet need. The incorporation of diagnostic biomarkers highly specific for MS in the future may prevent misdiagnosis.

[SFBC Working Group on sources of errors in laboratory medicine: objectives and key areas of work]. / Groupe de travail SFBC Sur les sources d'erreurs en biologie clinique: Objectifs et axes de travail.

Laboratory medicine plays a crucial role in patient care, contributing to approximately 70 % of clinical decisions. In collaboration with clinicians, laboratory medicine specialists perform analyses that are useful for diagnosis, screening and prevention. Laboratories are known for their efficiency, which is reached through a rigorous quality system. However, errors can occur, especially given the complexity of the total testing process. These errors may lead to severe consequences, such as incorrect diagnoses or delays in treatment. Errors can occur at every stage of the total testing process, those related to the pre-analytical phase being the most prevalent. To reduce medical errors related to laboratory processes, it is essential to provide training for medical and paramedical staff, optimize production automation, and leverage technological advancements. These considerations have led to the creation of a French Working Group on Sources of Errors in Laboratory Medicine, under the aegis of the French lean society of clinical chemistry and laboratory medicine (Société Française de Biologie Clinique - SFBC). The objectives of this working group are to produce an educational handbook on sources of errors in laboratory medicine, provide training for clinical chemists, and conducting applied research projects to better understand the mechanisms behind specific errors. Ultimately, the aim is to minimize errors and enhance the quality of laboratory tests.

Machine Learning to Enhance Electronic Detection of Diagnostic Errors.

This cohort study examines whether machine learning (ML) can enhance the ability of electronic triggers to identify possible missed opportunities in diagnosis.

Interprofessional Coproduction of Diagnosis with Medical and Pharmacy Students: An Interactive Case-Based Workshop.

Introduction: The Institute of Medicine and national competencies emphasize the importance of interprofessional education to reduce diagnostic error. Clinical pharmacists are increasingly integrated into clinical teams and participate in the diagnostic process. However, few educational resources explicitly teach medical and pharmacy students to collaborate on the production of diagnoses. Methods: We implemented a 2-hour, online, case-based workshop with 154 second-year medical students and third-year pharmacy students. After brief didactics on the diagnostic process and scope of practice of pharmacists, small groups of eight to 12 medical and pharmacy students with faculty facilitators worked through a case unfolding in two aliquots. Students were provided different but complementary information authentic to their profession. They had to communicate with each other to develop an appropriate differential diagnosis. Students then reflected on how communicating with the other profession impacted their diagnostic reasoning. Comments were coded and counted. Results: The majority (99%) of students identified their data gathering and differential diagnoses were impacted by working through the case together. More pharmacy students commented on how medical students broadened their differential diagnosis (71%) and added information (72%), contextualizing information, such as past history, medication indications, and physical exam data. More medical students commented on how pharmacy students helped justify (54%) and clarify (22%) the differential diagnosis, often connecting the underlying mechanism of medications with clinical findings. Discussion: This interactive case-based workshop was effective in teaching medical and pharmacy students to collaborate in the coproduction of diagnosis. It is feasible with minimal resources.

Retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations in conjunction with systemic lupus erythematosus: Missed diagnosis or misdiagnosis?

BACKGROUND: Retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations (RVCL-S) is a rare autosomal dominant systemic microvascular disorder attributed to TREX1 (three-prime repair exonuclease-1) gene mutations, often proned to misdiagnosed. METHODS: We reported a case of RVCL-S coexisting with systemic lupus erythematosus due to a mutation in the TREX1 gene. This study provided a summary and discussion of previously documented cases related to TREX1 mutations or RVCL-S. RESULTS: A 39-year-old female patient visited the clinic due to progressive memory loss and speech difficulties. Magnetic resonance imaging results showed corpus callosum atrophy and multiple subcortical calcifications in both brain hemispheres. Genetic testing revealed a TREX1 gene mutation (c.294dupA). Treatment with immunosuppressive therapy for 2 months led to improvements in communication and mobility. We also summarized previously reported cases providing an overview of TREX1 gene mutation or RCVL-S. CONCLUSION: Our case establishes a compelling foundation for future RVCL-S diagnosis and treatment paradigms. Notably, conducting systemic immunity screening in patients with RVCL-S emerges as a strategic approach to prevent potential diagnostic oversights.

How is diagnostic uncertainty communicated and managed in real world primary care settings?

BACKGROUND: Managing diagnostic uncertainty is a major challenge in primary care due to factors such as the absence of definitive tests, variable symptom presentations and disease evolution. Maintaining patient trust during a period of investigative uncertainty, whilst minimising scope for diagnostic error is a challenge. Mismanagement can lead to diagnostic errors, treatment delays, and suboptimal patient outcomes. OBJECTIVE: Our aim was to explore how UK primary care physicians (GPs) address and communicate diagnostic uncertainty in practice. DESIGN: This qualitative study used video and audio-recordings. Verbatim transcripts were coded with a modified, validated tool to capture GPs' actions and communication in primary care consultations that included diagnostic uncertainty. The tool includes items relating to advice regarding new symptoms or symptom deterioration (sometimes called 'safety netting'). Video data was analysed to identify GP and patient body postures during and after the delivery of the management plan. PARTICIPANTS: All patient participants had a consultation with a GP, were over the age of 50 and had (1) at least one new presenting problem or (2) one persistent problem that was undiagnosed. APPROACH: Data collection occurred in GP-patient consultations during 2017-2018 across 7 practices in UK during 2017-2018. KEY RESULTS: GPs used various management strategies to address diagnostic uncertainty, including (1) symptom monitoring without treatment, (2) prescribed treatment with symptom monitoring, and (3) addressing risks that could arise from administrative tasks. GPs did not make management plans for potential treatment side effects. Specificity of uncertainty management plans varied among GPs, with only some offering detailed actions and timescales. The transfer of responsibility for the management plan to patients was usually delivered rather than negotiated, with most patients confirming acceptance before concluding the discussion. CONCLUSIONS: We offer guidance to healthcare professionals, improving awareness of using and communicating management plans for diagnostic uncertainty.

CLINICAL REASONING AND ARTIFICIAL INTELLIGENCE: CAN AI REALLY THINK?

Artificial intelligence (AI) in the form of ChatGPT has rapidly attracted attention from physicians and medical educators. While it holds great promise for more routine medical tasks, may broaden one's differential diagnosis, and may be able to assist in the evaluation of images, such as radiographs and electrocardiograms, the technology is largely based on advanced algorithms akin to pattern recognition. One of the key questions raised in concert with these advances is: What does the growth of artificial intelligence mean for medical education, particularly the development of critical thinking and clinical reasoning? In this commentary, we will explore the elements of cognitive theory that underlie the ways in which physicians are taught to reason through a diagnostic case and compare hypothetico-deductive reasoning, often employing illness scripts, with inductive reasoning, which is based on a deeper understanding of mechanisms of health and disease. Issues of cognitive bias and their impact on diagnostic error will be examined. The constructs of routine and adaptive expertise will also be delineated. The application of artificial intelligence to diagnostic problem solving, along with concerns about racial and gender bias, will be delineated. Using several case examples, we will demonstrate the limitations of this technology and its potential pitfalls and outline the direction medical education may need to take in the years to come.

Diagnostic Stewardship: AJR Podcast Series on Diagnostic Excellence and Error, Episode 3.

In this episode of the AJR Podcast Series on Diagnostic Excellence and Error, Francis Deng, MD, and Daniel Morgan, MD, MS, discuss diagnostic stewardship. They explore how improving diagnostic test ordering, processing, and reporting can reduce error and improve outcomes and address practical strategies and radiologists' role in implementing stewardship.

Clinical reasoning-the essentials for teaching medical students, trainees and non-medical healthcare professionals.

Clinical reasoning is fundamental for effective clinical practice. Traditional consultation models for teaching clinical reasoning or conventional approaches for teaching students how to make a diagnosis or management plan that rely on learning through observation only, are increasingly recognised as insufficient. There are also many challenges to supporting learners in developing clinical reasoning over time as well as across different clinical presentations and contexts. These challenges are compounded by the differences in how experts and novices make sense of clinical information, and the different cognitive processes each use when processing and communicating this information using precise medical language. Diagnostic errors may be due to cognitive biases but also, in a majority of cases, due to a lack of clinical knowledge. Therefore, effective educational strategies to develop clinical reasoning include identifying learners' knowledge gaps, using worked examples to prevent cognitive overload, promoting the use of key features and practising the construction of accurate problem representations. Deliberate reflection on diagnostic justification is also recommended, and overall, contributes to a growing number of evidence-based and theory-driven educational interventions for reducing diagnostic errors and improving patient care.

Diagnostic Discrepancies in the Emergency Department: A Retrospective Study.

OBJECTIVES: Diagnostic errors contribute substantially to preventable medical errors. Especially, the emergency department (ED) is a high-risk environment. Previous research showed that in 15%-30% of the ED patients, there is a difference between the primary diagnosis assigned by the emergency physician and the discharge diagnosis. This study aimed to determine the number and types of diagnostic discrepancies and to explore factors predicting discrepancies. METHODS: A retrospective record review was conducted in an academic medical center. The primary diagnosis assigned in the ED was compared with the discharge diagnosis after hospital admission. For each patient, we gathered additional information about the diagnostic process to identify possible predictors of diagnostic discrepancies. RESULTS: The electronic health records of 200 patients were reviewed. The primary diagnosis assigned in the ED was substantially different from the discharge diagnosis in 16.0%. These diagnostic discrepancies were associated with a higher number of additional diagnostics applied for (2.4 versus 2.0 diagnostics; P = 0.002) and longer stay in the ED (5.9 versus 4.7 hours; P = 0.008). CONCLUSIONS: A difference between the diagnosis assigned by the emergency physician and the discharge diagnosis was found in almost 1 in 6 patients. The increased number of additional diagnostics and the longer stay at the ED in the group of patients with a diagnostic discrepancy suggests that these cases reflect the more difficult cases. More research should be done on predictive factors of diagnostic discrepancies.

Understanding Diagnostic Errors: AJR Podcast Series on Diagnostic Excellence and Error, Episode 2.

In this episode of the AJR Podcast Series on Diagnostic Excellence and Error, Francis Deng, MD, and David Newman-Toker, MD, PhD, discuss the prevalence and harms of diagnostic error in medicine; the role and causes of error in diagnostic radiology; and disease-specific examples, including in patients with stroke who present with dizziness.

Pitfalls in Gynecological Cytology: Review of the Common and Less Frequent Entities in Pap Test.

BACKGROUND: Pitfalls in Pap test could be defined as false positive, false negative, or underdiagnosed results which can lead to unnecessary diagnostic procedures or delayed and inadequate treatment. It can be a consequence of misinterpretation of certain morphological entities which are described in this paper. SUMMARY: The paper presents an overview of the morphological features and look-alikes of the common sources of pitfalls such as atrophy, repair, intrauterine device change, tubal metaplasia, hyperchromatic crowded groups, and radiation changes. Rare causes of pitfalls such as Arias-Stella changes, pemphigus, tumor diathesis per se, rare types of cervical cancer, including verrucous and papillary squamous cell cancer, gastric type, and endometrioid adenocarcinoma are also described. KEY MESSAGES: The awareness of pitfalls in cervical cytology is important for cytopathologists and clinicians to avoid future errors. Review of Pap tests with erroneous diagnosis is important for quality control in cytology laboratory, and it must be considered an educational- and experience-building procedure. Cytopathologist should not pull back in significant diagnoses, especially in human papillomavirus-negative cases.

What Is Diagnostic Excellence? AJR Podcast Series on Diagnostic Excellence and Error, Episode 1.

In this episode of the AJR Podcast Series on Diagnostic Excellence and Error, Francis Deng, MD, introduces the concept of diagnostic excellence and its relevance to radiologists. Patient-centered definitions of diagnostic error and conceptualizations of the diagnostic process are discussed.

In this episode of the AJR Podcast Series on Diagnostic Excellence and Error, Francis Deng, MD, introduces the concept of diagnostic excellence and its relevance to radiologists. Patient-centered definitions of diagnostic error and conceptualizations of the diagnostic process are discussed.

Spectrum of Cognitive Biases in Diagnostic Radiology.

Cognitive biases are systematic thought processes involving the use of a filter of personal experiences and preferences arising from the tendency of the human brain to simplify information processing, especially when taking in vast amounts of data such as from imaging studies. These biases encompass a wide spectrum of thought processes and frequently overlap in their concepts, with multiple biases usually in operation when interpretive and perceptual errors occur in radiology. The authors review the gamut of cognitive biases that occur in radiology. These biases are organized according to their expected stage of occurrence while the radiologist reads and interprets an imaging study. In addition, the authors propose several additional cognitive biases that have not yet, to their knowledge, been defined in the radiologic literature but are applicable to diagnostic radiology. Case examples are used to illustrate potential biases and their impact, with emergency radiology serving as the clinical paradigm, given the associated high imaging volumes, wide diversity of imaging examinations, and rapid pace, which can further increase a radiologist's reliance on biases and heuristics. Potential strategies to recognize and overcome one's personal biases at each stage of image interpretation are also discussed. Awareness of such biases and their unintended effects on imaging interpretations and patient outcomes may help make radiologists cognizant of their own biases that can result in diagnostic errors. Identification of cognitive bias in departmental and systematic quality improvement practices may represent another tool to prevent diagnostic errors in radiology. ©RSNA, 2024 See the invited commentary by Larson in this issue.

Overcoming Pitfalls in Breast Fine-Needle Aspiration Cytology: A Practical Review.

BACKGROUND: Fine-needle aspiration cytology (FNAC) is a cornerstone technique for the initial assessment of breast lesions, offering a rapid and minimally invasive option for cytological evaluation. While FNACs can forego the need for core needle biopsies (CNBs), variations in technique, subjective interpretation, and intrinsic limitations present diagnostic challenges. The International Academy of Cytology (IAC) established the Yokohama system and is developing the WHO Reporting System for Breast Cytopathology jointly with IARC, to standardize diagnostic criteria, aiming to enhance diagnostic precision and consistency. Due to the preference for CNBs, expertise in breast FNAC is low in the developed world. SUMMARY: This review assesses common pitfalls in breast cytopathology. These common and uncommon entities may easily lead to false-negative or false-positive diagnoses, due to morphological overlap or misleading clinical and radiological contexts. For instance, pauci-cellular lesions, such as lobular carcinomas, often lead to false-negative diagnoses, whereas complex sclerosing lesions, fibroadenomas, and papillary lesions may show concerning features, resulting in a false positive. The same is true for some benign inflammatory pathologies, such as steatonecrosis, and uncommon lesions, such as collagenous spherulosis. Ductal carcinoma in situ can lead to both false-negative and false-positive diagnoses, and high-grade lesions are impossible to tell apart from invasive carcinomas. These are discussed in detail. Procedural and preanalytical conditions, and the role of ancillary testing, are also briefly addressed. KEY MESSAGES: Breast FNAB is a powerful diagnostic technique, fast and minimally invasive. Even in contexts which lack expertise, this technique can be successfully adopted with a cautious approach and as long as pitfalls are kept in mind, benefiting patients and healthcare systems.

[Possibilities of using results of head computed tomography in victims with traumatic brain injuries]. / Vozmozhnosti ispol'zovaniya rezul'tatov komp'yuternoi tomografii golovy u poterpevshikh s cherepno-mozgovoi travmoi.

The constant increase in the number of neurotraumas in the country leads to an increase in forensic examinations of a persons. In Russia, about 600 thousand people receive craniocerebral injuries annually, of which 50 thousand die, others are potentially will be in forensic examination during or after treatment. With an increase in the total number of such examinations, the number of erroneous conclusions is expected to increase. If it is impossible for the radiologist included in the commission to review the results of computed tomography of the head performed in the hospital, the experts are forced to use the data that are recorded in the medical documents. The present study revealed the percentage of erroneous interpretations in such descriptions, systematized typical errors, calculated the sensitivity, specificity and accuracy of computed tomography in craniocerebral injury.

Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research.

Background: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. Objective: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process. Methods: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). Results: ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048). Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings.

Words matter: Comments in support of diagnosis education in pharmacy.

Diagnosis education is explicitly included in the Draft Accreditation Standards 2025 and should be wholeheartedly supported. To address the staggering number of diagnostic errors in the US, the National Academy of Medicine advocates for diagnosis education for all health professions. Misperceptions of pharmacists' involvement in diagnosis are exacerbated by use of implicit language and euphemisms, and it's long overdue that we provide clarity. Pharmacists are engaged in diagnosis and diagnostic reasoning in everyday practice whether realized or not. Diagnosis education is implicitly included in US pharmacy curricula in the Pharmacists' Patient Care Process, and it is time to give students the language to engage in it responsibly to be practice-ready in all states. The explicit inclusion of diagnosis in the Draft Standards 2025 has positive implications for education, practice, and graduates.