[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.

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.

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.

Three scans are better than two for follow-up: An automatic method for finding missed and misidentified lesions in cross-sectional follow-up of oncology patients.

PURPOSE: Missed and misidentified neoplastic lesions in longitudinal studies of oncology patients are pervasive and may affect the evaluation of the disease status. Two newly identified patterns of lesion changes, lone lesions and non-consecutive lesion changes, may help radiologists to detect these lesions. This study evaluated a new interpretation revision workflow of lesion annotations in three or more consecutive scans based on these suspicious patterns. METHODS: The interpretation revision workflow was evaluated on manual and computed lesion annotations in longitudinal oncology patient studies. For the manual revision, a senior radiologist and a senior neurosurgeon (the readers) manually annotated the lesions in each scan and later revised their annotations to identify missed and misidentified lesions with the workflow using the automatically detected patterns. For the computerized revision, lesion annotations were first computed with a previously trained nnU-Net and were then automatically revised with an AI-based method that automates the workflow readers' decisions. The evaluation included 67 patient studies with 2295 metastatic lesions in lung (19 patients, 83 CT scans, 1178 lesions), liver (18 patients, 77 CECT scans, 800 lesions) and brain (30 patients, 102 T1W-Gad MRI scans, 317 lesions). RESULTS: Revision of the manual lesion annotations revealed 120 missed lesions and 20 misidentified lesions in 31 out of 67 (46%) studies. The automatic revision reduced the number of computed missed lesions by 55 and computed misidentified lesions by 164 in 51 out of 67 (76%) studies. CONCLUSION: Automatic analysis of three or more consecutive volumetric scans helps find missed and misidentified lesions and may improve the evaluation of temporal changes of oncological lesions.

Mistakes in utilising histopathology for the management of liver disease.

INTRODUCTION: Liver biopsy has become selective due to its invasiveness, potential adverse effects, patient acceptance and cost. Furthermore, the emergence of noninvasive tests (NITs) has challenged the necessity of liver biopsies in specific clinical situations. However, liver biopsy continues to play a crucial role in disease diagnosis, prognosis, and evaluating treatment compliance and response in selected patients. AREAS COVERED: In this narrative review, we discuss the errors and the shortcomings that can occur at various stages, from the initial patient selection for a liver biopsy to the final reporting phase, and strategies to address them. Clinicians and pathologists must take all necessary precautions to mitigate potential shortcomings that could compromise the value of liver biopsies. EXPERT OPINION: The increasing sophistication of NITs offers a safer, more convenient, and potentially more cost-effective approach to diagnosing chronic liver disease, especially for assessing the degree of liver fibrosis. As NITs continue to evolve, liver biopsy will likely transition to a more targeted role, ensuring optimal patient care in the ever-changing field of hepatology. However, liver biopsy will continue to have a pivotal role in assessing acute liver disease where the diagnostic yield of the liver biopsy still outweighs that of NITs.

Stakeholder perspectives on contributors to delayed and inaccurate diagnosis of cardiovascular disease and their implications for digital health technologies: a UK-based qualitative study.

OBJECTIVE: The aim of this study is to understand stakeholder experiences of diagnosis of cardiovascular disease (CVD) to support the development of technological solutions that meet current needs. Specifically, we aimed to identify challenges in the process of diagnosing CVD, to identify discrepancies between patient and clinician experiences of CVD diagnosis, and to identify the requirements of future health technology solutions intended to improve CVD diagnosis. DESIGN: Semistructured focus groups and one-to-one interviews to generate qualitative data that were subjected to thematic analysis. PARTICIPANTS: UK-based individuals (N=32) with lived experience of diagnosis of CVD (n=23) and clinicians with experience in diagnosing CVD (n=9). RESULTS: We identified four key themes related to delayed or inaccurate diagnosis of CVD: symptom interpretation, patient characteristics, patient-clinician interactions and systemic challenges. Subthemes from each are discussed in depth. Challenges related to time and communication were greatest for both stakeholder groups; however, there were differences in other areas, for example, patient experiences highlighted difficulties with the psychological aspects of diagnosis and interpreting ambiguous symptoms, while clinicians emphasised the role of individual patient differences and the lack of rapport in contributing to delays or inaccurate diagnosis. CONCLUSIONS: Our findings highlight key considerations when developing digital technologies that seek to improve the efficiency and accuracy of diagnosis of CVD.

AI in radiology: Legal responsibilities and the car paradox.

The integration of AI in radiology raises significant legal questions about responsibility for errors. Radiologists fear AI may introduce new legal challenges, despite its potential to enhance diagnostic accuracy. AI tools, even those approved by regulatory bodies like the FDA or CE, are not perfect, posing a risk of failure. The key issue is how AI is implemented: as a stand-alone diagnostic tool or as an aid to radiologists. The latter approach could reduce undesired side effects. However, it's unclear who should be held liable for AI failures, with potential candidates ranging from engineers and radiologists involved in AI development to companies and department heads who integrate these tools into clinical practice. The EU's AI Act, recognizing AI's risks, categorizes applications by risk level, with many radiology-related AI tools considered high risk. Legal precedents in autonomous vehicles offer some guidance on assigning responsibility. Yet, the existing legal challenges in radiology, such as diagnostic errors, persist. AI's potential to improve diagnostics raises questions about the legal implications of not using available AI tools. For instance, an AI tool improving the detection of pediatric fractures could reduce legal risks. This situation parallels innovations like car turn signals, where ignoring available safety enhancements could lead to legal problems. The debate underscores the need for further research and regulation to clarify AI's role in radiology, balancing innovation with legal and ethical considerations.

Frontline Providers' and Patients' Perspectives on Improving Diagnostic Safety in the Emergency Department: A Qualitative Study.

BACKGROUND: Few studies have described the insights of frontline health care providers and patients on how the diagnostic process can be improved in the emergency department (ED), a setting at high risk for diagnostic errors. The authors aimed to identify the perspectives of providers and patients on the diagnostic process and identify potential interventions to improve diagnostic safety. METHODS: Semistructured interviews were conducted with 10 ED physicians, 15 ED nurses, and 9 patients/caregivers at two separate health systems. Interview questions were guided by the ED-Adapted National Academies of Sciences, Engineering, and Medicine Diagnostic Process Framework and explored participant perspectives on the ED diagnostic process, identified vulnerabilities, and solicited interventions to improve diagnostic safety. The authors performed qualitative thematic analysis on transcribed interviews. RESULTS: The research team categorized vulnerabilities in the diagnostic process and intervention opportunities based on the ED-Adapted Framework into five domains: (1) team dynamics and communication (for example, suboptimal communication between referring physicians and the ED team); (2) information gathering related to patient presentation (for example, obtaining the history from the patients or their caregivers; (3) ED organization, system, and processes (for example, staff schedules and handoffs); (4) patient education and self-management (for example, patient education at discharge from the ED); and (5) electronic health record and patient portal use (for example, automatic release of test results into the patient portal). The authors identified 33 potential interventions, of which 17 were provider focused and 16 were patient focused. CONCLUSION: Frontline providers and patients identified several vulnerabilities and potential interventions to improve ED diagnostic safety. Refining, implementing, and evaluating the efficacy of these interventions are required.

Stigmatizing Language, Patient Demographics, and Errors in the Diagnostic Process.

This cohort study assesses the association between stigmatizing language, demographic characteristics, and errors in the diagnostic process among hospitalized adults.

The influence of viewing time on visual diagnostic accuracy: Less is more.

BACKGROUND: Understanding the factors that contribute to diagnostic errors is critical if we are to correct or prevent them. Some scholars influenced by the default interventionist dual-process theory of cognition (dual-process theory) emphasise a narrow focus on individual clinician's faulty reasoning as a significant contributor. In this paper, we examine the validity of claims that dual process theory is a key to error reduction. METHODS: We examined the relationship between a clinical experience (staff and resident physicians) and viewing time on accuracy for categorising chest X-rays (CXRs) and electrocardiograms (ECGs). In two studies, participants categorised images as normal or abnormal, presented at viewing times of 175, 250, 500 and 1000 ms, to encourage System 1 processing. Study 2 extended viewing times to 1, 5, 10 and 20 s to allow time for System 2 processing and a diagnosis. Descriptives and repeated measures analysis of variance were used to analyse the proportion of true and false positive rates (TP and FP) as well as correct diagnoses. RESULTS: In Study 1, physicians were able to detect abnormal CXRs (0.78) and ECGs (0.67) with relatively high accuracy. The effect of experience was found for ECGs only, as staff physicians (0.71, 95% CI = 0.66-0.75) had higher ECG TP than resident physicians (0.63, 95% CI = 0.58-0.68) in Study 1, and staff had lower ECG FP (0.10, 95% CI = 0.03-0.18) than resident physicians (0.27, 95% CI = 0.20-0.33) in Study 2. In other comparisons, experience was equivocal for ECG FPs and CXR TPs and FPs. In Study 2, overall diagnostic accuracy was similar for both ECGs and CXRs, (0.74). There were small interactions between experience and time for TP in ECGs and FP in CXRs, which are discussed further in the discussion and offer insights into the relationship between processing and experience. CONCLUSION: Overall, our findings raise concerns about the practical application of models that link processing type to diagnostic error, or to specific diagnostic error reduction strategies.

Is Myocardial Infarction Overdiagnosed?

This Viewpoint examines whether overdiagnosis rather than underdiagnosis may now be the dominant form of myocardial infarction misdiagnosis.

Refining a Framework to Enhance Communication in the Emergency Department During the Diagnostic Process: An eDelphi Approach.

BACKGROUND: Emergency departments (EDs) are susceptible to diagnostic error. Suboptimal communication between the patient and the interdisciplinary care team increases risk to diagnostic safety. The role of communication remains underrepresented in existing diagnostic decision-making conceptual models. METHODS: The authors used eDelphi methodology, whereby data are collected electronically, to achieve consensus among an expert panel of 18 clinicians, patients, family members, and other participants on a refined ED-based diagnostic decision-making framework that integrates several potential opportunities for communication to enhance diagnostic quality. This study examined the entire diagnostic process in the ED, from prehospital to discharge or transfer to inpatient care, and identified where communication breakdowns could occur. After four iterative rounds of the eDelphi process, including a final validation round by all participants, the project's a priori consensus threshold of 80% agreement was reached. RESULTS: The authors developed a final framework that positions communication more prominently in the diagnostic process in the ED and enhances the original National Academies of Sciences, Engineering, and Medicine (NASEM) and ED-adapted NASEM frameworks. Specific points in the ED journey were identified where more attention to communication might be helpful. Two specific types of communication-information exchange and shared understanding-were identified as high priority for optimal outcomes. Ideas for communication-focused interventions to prevent diagnostic error in the ED fell into three categories: patient-facing, clinician-facing, and system-facing interventions. CONCLUSION: This project's refinement of the NASEM framework adapted to the ED can be used to develop communications-focused interventions to reduce diagnostic error in this highly complex and error-prone setting.

Lessons in clinical reasoning - pitfalls, myths, and pearls: a woman brought to a halt.

OBJECTIVES: Limitations in human cognition commonly result in clinical reasoning failures that can lead to diagnostic errors. A metacognitive structured reflection on what clinical findings fit and/or do not fit with a diagnosis, as well as how discordance of data can help advance the reasoning process, may reduce such errors. CASE PRESENTATION: A 60-year-old woman with Hashimoto thyroiditis, diabetes, and generalized anxiety disorder presented with diffuse arthralgias and myalgias. She had been evaluated by physicians of various specialties and undergone multiple modalities of imaging, as well as a electromyography/nerve conduction study (EMG/NCS), leading to diagnoses of fibromyalgia, osteoarthritis, and lumbosacral plexopathy. Despite treatment for these conditions, she experienced persistent functional decline. The only definitive alleviation of her symptoms identified was in the few days following intra-articular steroid injections for osteoarthritis. On presentation to our institution, she appeared fit with a normal BMI. She was a long-time athlete and had been training consistently until her symptoms began. Prediabetes had been diagnosed the year prior and her A1c progressed despite lifestyle modifications and 10 pounds of intentional weight loss. She reported fatigue, intermittent nausea without emesis, and reduced appetite. Examination revealed intact strength and range of motion in both the shoulders and hips, though testing elicited pain. She had symmetric hyperreflexia as well as a slowed, rigid gait. Autoantibody testing revealed strongly positive serum GAD-65 antibodies which were confirmed in the CSF. A diagnosis of stiff-person syndrome was made. She had an incomplete response to first-line therapy with high-dose benzodiazepines. IVIg was initiated with excellent response and symptom resolution. CONCLUSIONS: Through integrated commentary on the diagnostic reasoning process from clinical reasoning experts, this case underscores the importance of frequent assessment of fit along with explicit explanation of dissonant features in order to avoid misdiagnosis and halt diagnostic inertia. A fishbone diagram is provided to visually demonstrate the major factors that contributed to the diagnostic error. The case discussant demonstrates the power of iterative reasoning, case progression without commitment to a single diagnosis, and the dangers of both explicit and implicit bias. Finally, this case provides clinical teaching points in addition to a pitfall, myth, and pearl specific to overcoming diagnostic inertia.

Histopathologic evaluation of gastric intestinal metaplasia in non-neoplastic biopsy specimens: Accuracy and interobserver reliability among general pathologists and pathology residents.

OBJECTIVES: This study aimed to evaluate the accuracy and interobserver reliability of diagnosing and subtyping gastric intestinal metaplasia (IM) among general pathologists and pathology residents at a university hospital in Thailand, focusing on the challenges in the histopathologic evaluation of gastric IM for less experienced practitioners. METHODS: The study analyzed 44 non-neoplastic gastric biopsies, using a consensus diagnosis of gastrointestinal pathologists as the reference standard. Participants included 6 general pathologists and 9 pathology residents who assessed gastric IM and categorized its subtype (complete, incomplete, or mixed) on digital slides. After initial evaluations and receiving feedback, participants reviewed specific images of gastric IM, as agreed by experts. Following a one-month washout period, a reevaluation of the slides was conducted. RESULTS: Diagnostic accuracy, interobserver reliability, and time taken for diagnosis improved following training, with general pathologists showing higher accuracies than residents (median accuracy of gastric IM detection: 100 % vs. 97.7 %). Increased years of experience were associated with more IM detection accuracy (p-value<0.05). However, the overall median accuracy for diagnosing incomplete IM remained lower than for complete IM (86.4 % vs. 97.7 %). After training, diagnostic errors occurred in 6 out of 44 specimens (13.6 %), reported by over 40 % of participants. Errors involved omitting 5 slides with incomplete IM and 1 with complete IM, all showing a subtle presence of IM. CONCLUSIONS: The study highlights the diagnostic challenges in identifying incomplete gastric IM, showing notable discrepancies in accuracy and interobserver agreement. It underscores the need for better diagnostic protocols and training to enhance detection and management outcomes.

Ethical considerations for artificial intelligence in dermatology: a scoping review.

The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.

The use of artificial intelligence (AI) in dermatology is rapidly increasing, with applications in dermatopathology, medical dermatology, cutaneous surgery, microscopy/spectroscopy and the identification of prognostic biomarkers (characteristics that provide information on likely patient health outcomes). However, with the rise of AI in dermatology, ethical concerns have emerged. We reviewed the existing literature to identify applications of AI in the field of dermatology and understand the ethical implications. Our search initially identified 202 papers, and after we went through them (screening), 68 were included in our review. We found that ethical concerns are related to the use of AI in the areas of clinical image analysis, teledermatology, natural language processing models, privacy, skin of colour representation, and patient and provider attitudes toward AI. We identified nine ethical principles to facilitate the safe use of AI in dermatology. These ethical principles include fairness, inclusivity, transparency, accountability, security, privacy, reliability, informed consent and conflict of interest. Although there are many benefits of integrating AI into clinical practice, our findings highlight how safeguards must be put in place to reduce rising ethical concerns.

Exploring Clinical Lessons Learned by Experienced Hospitalists from Diagnostic Errors and Successes.

BACKGROUND: Diagnostic errors cause significant patient harm. The clinician's ultimate goal is to achieve diagnostic excellence in order to serve patients safely. This can be accomplished by learning from both errors and successes in patient care. However, the extent to which clinicians grow and navigate diagnostic errors and successes in patient care is poorly understood. Clinically experienced hospitalists, who have cared for numerous acutely ill patients, should have great insights from their successes and mistakes to inform others striving for excellence in patient care. OBJECTIVE: To identify and characterize clinical lessons learned by experienced hospitalists from diagnostic errors and successes. DESIGN: A semi-structured interview guide was used to collect qualitative data from hospitalists at five independently administered hospitals in the Mid-Atlantic area from February to June 2022. PARTICIPANTS: 12 academic and 12 community-based hospitalists with ≥ 5 years of clinical experience. APPROACH: A constructivist qualitative approach was used and "reflexive thematic analysis" of interview transcripts was conducted to identify themes and patterns of meaning across the dataset. RESULTS: Five themes were generated from the data based on clinical lessons learned by hospitalists from diagnostic errors and successes. The ideas included appreciating excellence in clinical reasoning as a core skill, connecting with patients and other members of the health care team to be able to tap into their insights, reflecting on the diagnostic process, committing to growth, and prioritizing self-care. CONCLUSIONS: The study identifies key lessons learned from the errors and successes encountered in patient care by clinically experienced hospitalists. These findings may prove helpful for individuals and groups that are authentically committed to moving along the continuum from diagnostic competence towards excellence.