Diagnostic Judgments Research Articles

Differential diagnosis of acute coronary syndrome without ST-segment elevation in a middle-aged patient

Acute Coronary Syndrome (ACS) is an initial diagnosis, which is transformed into the diagnosis of “myocardial infarction”, “unstable angina” or other diagnosis during the diagnostic process. If a patient meets the criteria for myocardial infarction (according to the Fourth Universal Definition of Myocardial Infarction), in the presence of atherothrombosis in the infarct-related coronary artery, Type 1 myocardial infarction is diagnosed. In most of the remaining cases Type 2 myocardial infarction is diagnosed. Acute myocardial injury due to various conditions is separately classified. In the presented case, a 54-year-old patient with a history of arterial hypertension and diabetes mellitus, not adherent to treatment and suffering from obesity, a smoker, was hospitalized with typical new-onset angina pectoris and ischemic changes on the ECG, by ambulance, with the initial diagnosis of “ACS without ST-segment elevation”. The diagnosis changed several times during the examination: “ACS without ST-segment elevation”, “CAD: myocardial infarction without ST-segment elevation”, “Myocardial infarction with non-obstructive coronary arteries (MINOCA)”, “Severe calcified aortic stenosis. Anemia. Type 2 myocardial infarction”. The peculiarity of this case is the debut of high-gradient aortic stenosis in a middle-aged man with clinical manifestations of ACS and high blood troponin level. Causes of severe aortic stenosis manifestation as myocardial infarction with elevation of cardiac-specific troponin in the blood, despite intact coronary vessels, are discussed in this article. The differential diagnosis of myocardial injury is discussed, as the correct diagnostic judgment directly determines the patient’s management strategy.

How context influences the processing of relevant information and judgment accuracy-the role of information restriction in judgment processes in diagnosing misconceptions.

To adapt teaching to the prerequisites of students, teachers have various options at their disposal to gather and process information as the basis to form a judgment, such as carrying out tests, talking to and observing the behavior of students, or administering tasks. The complexity of such a judgment arises from the multitude of observations and their different possible explanations. This complexity might be reduced when teachers focus on one hypothesis instead of considering multiple hypotheses, interpret information in a confirmatory way, and not collect diagnostically relevant information. However, in this way, they run the risk of undesirable biased judgments. It therefore seems important to improve diagnostic judgments by selecting and processing information in a more reflective way. Research indicates that if information on a student is not easily available but restricted (e.g., by time pressure, difficult access to the student or high effort), a teacher who wants to make a careful decision is forced to rely on more reflective processes in the selection of tasks and in the interpretation of solutions. The present experimental study therefore investigates how the restricted availability of information in a specific diagnostic situation-when diagnostically inexperienced prospective mathematics teachers determine misconceptions in decimal fractions-influences the underlying cognitive processes. We assume that restricting the availability of information on student behavior augments the attentional focus and therefore reduces cognitive biases. Such more reflective processing can be observed by an increased time spent per piece of information, which should lead to the processing of relevant information and further increase judgment accuracy. To investigate these hypotheses, prospective teachers without prior knowledge in diagnosing misconceptions (N = 81) were asked to diagnose misconceptions on decimal fractions of virtual students by collecting information on students' solutions. Data concerning the effects of restricting the availability of information on teachers' cognitive processes were analyzed. The results show that with restricted information, participants indeed select a greater proportion of diagnostically relevant tasks, which positively influences judgment accuracy. These results are discussed with respect to their significance for framing teacher training and for further research.

Diagnostic Hypotheses and Practical Judgments: Towards a Characterization of Clinical Reasoning

Diagnostic Hypotheses and Practical Judgments: Towards a Characterization of Clinical Reasoning

Artificial Intelligence in Healthcare With an Emphasis on Public Health.

Artificial Intelligence (AI), since its inception, has revolutionized multiple sectors, including healthcare in the 20th century, and has applications in data interpretation, leading to advancements in diagnostics, therapeutics, and clinical decision-making.AI is referred to as the capability of a software program to accurately analyze extrinsic information and utilize it for accomplishing desired goals and objectives through appropriate flexibility. It makes use of complicated operative algorithms to excel in humanlearning potential with overwhelming abilities to interpret large sets of data. The scope and implications of AI are consistently amplifying and have contributed significantly in nearly all phases of human life, especially healthcare. The integration of AI-related advancements would surely ameliorate the delivery of healthcare by allowing its accessibility, affordability, and level of care provided. For instance, reading CT scans is feasible by both AI as well as a radiologist. The screening of Tuberculosis is possible through AI via Chest X-rays with comparability in performance as molecular testing and mammography scans can predict the onset of breast cancer prior to the appearance of the ocular signs. Therefore, AI has been realized as one of the core areas by researchers and the governmentfor public health benefit. For the same reason, it is imperative to adopt an ethically sound policy framework for guiding the further development of AI-based technologies and their application in public health. AI-based interpretations themselves cannot be fully trusted for their diagnostic decisions and judgements, and hence, it is vital to assess their accountability through all phases of development and deployment in the field of health. This article emphasizes the advancements in AI-based technologies, their assistance in public healthcare delivery systems, and their merits and demerits. It also explores the various ethical directives that need to be adhered to while utilizing it for public health welfare.

A case of severe combined damage to the pancreas and liver as a result of prolonged alcohol abuse

A case of severe combined damage to the pancreas and liver caused by prolonged alcohol abuse is presented. Acute pancreatitis in the structure of emergency surgical pathology of the abdominal cavity is 25%. The frequency of erroneous diagnostic judgments reaches 35%. In this case, at the beginning, the disease was unequivocally regarded as acute alcoholic hepatitis, which led to a late diagnosis of acute pancreatitis. However, complex conservative therapy proved to be quite effective.

Preservice teachers' judgments of students’ expectations of success and task values: Close relations with their personal task motivation

Little is known about the mechanisms underlying preservice teachers' judgments of students' task motivation, which is relevant to instructional planning. In this study, 64 preservice teachers rated their personal expectations of success and task values, and they judged average ninth grade students' expectations of success and task values associated with four modeling and four intramathematical problems. Preservice teachers judged students' task values lower than their personal task values. In the ranking of problems, the correlation between personal and judged task motivation was strongly positive, indicating that preservice teachers’ personal task motivation is a powerful determinant of their diagnostic judgments.

Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education.

The impact of artificial intelligence (AI) will be felt not only in the arenaof patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.

Comparing the DSM-5 categorical model of personality disorders and the alternative model of personality disorders regarding clinician judgments of risk and outcome.

The goal of this study was to compare the predictive validity of the alternative model for personality disorders (AMPD) versus the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) Section II categorical model regarding clinician judgments about mental health outcomes. To do so, we instructed a national sample of 136 mental health professionals to provide clinical judgments on a random subset of four (out of a possible 12) case vignettes. For each case, they made a variety of diagnostic judgments corresponding to each model, as well as clinical outcome judgments (e.g., prognosis). Our analyses included hierarchical and individual regressions to compare the predictive value of each diagnostic system toward these clinical outcome judgments. We found that the AMPD predictors consistently added unique variance beyond the Section II predictors, whereas the Section II predictors were rarely incremental above the AMPD. Further, the AMPD judgments predicted outcome judgments very consistently (98.3% of regressions) compared to the Section II predictors (70% of regressions), and the single Criterion A judgment (level of personality functioning) was the strongest overall predictor. Finally, the categorical borderline personality disorder diagnoses from the two systems performed similarly in predicting clinical outcomes and agreed in 79% of cases. We interpreted our results to suggest that the AMPD is at least as effective, and by some measures more effective, than the DSM-5 categorical model at predicting clinician's judgment of outcomes in clinical cases. We conclude by discussing the value of this evidence in relation to the broader AMPD literature, as well as possible paths forward for the diagnosis of personality disorders. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Trust in Machine Learning Driven Clinical Decision Support Tools Among Otolaryngologists.

Machine learning driven clinical decision support tools (ML-CDST) are on the verge of being integrated into clinical settings, including in Otolaryngology-Head & Neck Surgery. In this study, we investigated whether such CDST may influence otolaryngologists' diagnostic judgement. Otolaryngologists were recruited virtually across the United States for this experiment on human-AI interaction. Participants were shown 12 different video-stroboscopic exams from patients with previously diagnosed laryngopharyngeal reflux or vocal fold paresis and asked to determine the presence of disease. They were then exposed to a random diagnosis purportedly resulting from an ML-CDST and given the opportunity to revise their diagnosis. The ML-CDST output was presented with no explanation, a general explanation, or a specific explanation of its logic. The ML-CDST impact on diagnostic judgement was assessed with McNemar's test. Forty-five participants were recruited. When participants reported less confidence (268 observations), they were significantly (p = 0.001) more likely to change their diagnostic judgement after exposure to ML-CDST output compared to when they reported more confidence (238 observations). Participants were more likely to change their diagnostic judgement when presented with a specific explanation of the CDST logic (p = 0.048). Our study suggests that otolaryngologists are susceptible to accepting ML-CDST diagnostic recommendations, especially when less confident. Otolaryngologists' trust in ML-CDST output is increased when accompanied with a specific explanation of its logic. 2 Laryngoscope, 134:2799-2804, 2024.

How pedagogical content knowledge sharpens prospective teachers’ focus when judging mathematical tasks: an eye-tracking study

Teachers’ ability to accurately judge difficulties of mathematical tasks is an essential aspect of their diagnostic competencies. Although research has suggested that pedagogical content knowledge (PCK) is positively correlated with the accuracy of diagnostic judgments, experimental studies have not been conducted to investigate how PCK affects perception and interpretation of relevant task characteristics. In an intervention study with a control group, 49 prospective mathematics teachers judged the difficulty of 20 tasks involving functions and graphs while an eye tracker tracked their eye movements. Some of the tasks included characteristics well known to be difficult for students. Participants’ domain-specific PCK of typical student errors was manipulated through a three-hour intervention, during which they learned about the most common student errors in function and graph problems. We found that the process of perception (relative fixation duration on the relevant area in the tasks) was related to judgment accuracy. Pre-post comparisons revealed an effect of the intervention not only on participants’ domain-specific PCK of typical student errors but also on their perception and interpretation processes. This result suggests that domain-specific PCK of typical student errors allowed participants to focus more efficiently on relevant task characteristics when judging mathematical task difficulties. Our study contributes to our understanding of how professional knowledge makes teachers’ judgment processes of mathematical tasks more efficient.

Children use epistemic states flexibly to make diagnostic social inferences.

Children are sensitive to their own and others' epistemic states and use these to guide their learning and communication. Here, we systematically examined children's use of epistemic states to make diagnostic social inferences. Specifically, we investigated children's group membership inferences based on what others do and do not know and what role children's own knowledge states and the type of knowledge play in these inferences. Across two preregistered studies, 7- and 8-year-old children (N = 100) were shown targets who were knowledgeable (Study 1) or ignorant (Study 2) of familiar and unfamiliar knowledge items that were either general or culture-specific and asked to guess whether the targets would be linguistic ingroup or outgroup members. Children inferred that the targets would speak their native language if they shared targets' epistemic states and a foreign language if they did not. Importantly, these patterns were particularly evident when epistemic states involved cultural knowledge. Further, children's inferences became more nuanced with age. These findings suggest that others' knowledge states are socially meaningful in childhood and children use their own and others' epistemic states in flexible ways to guide their diagnostic social judgments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Unveiling cognitive aspects and accuracy of diagnostic judgments in physical education teachers assessing different tasks in gymnastics

IntroductionDiagnostics is an essential part of teachers’ profession. We investigated judgment accuracy and cognitive processes underlying judgment formation in physical education teachers who observed tasks in gymnastics, and compared teachers with gymnastics trainers as a reference group.MethodsTeachers and trainers judged performance of prepuberal students in gymnastics, namely students exercising squat vault, underswing, and handstand. To investigate cognitive processes of judgment formation, participants were asked to structure the movements via event segmentation as well as to explain their judgments. All teachers and trainers had experience in working with prepuberal children similar to those they observed in this experiment, and the teachers completed a gymnastics class during their studies.ResultsJudgment accuracy (with reference to judgments made by expert trainers) was found to be significantly lower in teachers compared to trainers (p < 0.001). Moreover, agreement on the ratings among teachers was lower than among trainers. Agreement about the temporal structuring of the tasks from event segmentation was lower among teachers than among trainers (p < 0.05). When explaining their ratings, trainers referred more often than teachers to kinematic features of the task that were relevant to the judgments.DiscussionWe discuss these findings in context of the teachers’ task to perform accurate judgments. For suggestions on teacher training, we particularly emphasize the relevance of implementing knowledge about kinematic features of the tasks and student errors into real-life scenarios resembling the complex skill of making accurate judgments in the physical education classroom.

Companion responses to diagnosis in Chinese outpatient clinical interaction

Patients regularly attend clinical consultations with companions in Chinese outpatient clinics. Despite companions' significant influence on clinical consultations, how companions respond to diagnosis and their contributions to the activity of diagnosis in Chinese outpatient clinical interaction remain under-researched. The present study, by adopting the method of conversation analysis, investigated clinicians' diagnostic deliveries and companions' subsequent responses in Chinese outpatient clinical interaction. The data for this study consisted of 79 video recordings of clinical consultations in the Chinese orthopedic outpatient clinic, approximately lasting 12 h and involving three male clinicians, 79 patients (37 male/42 female), and 91 companions (51 male/40 female). Three basic categories of companion responses to diagnosis were identified: minimal verbal responses, embodied responses, and extended responses. It was demonstrated that these distinct responses allowed companions to challenge clinicians' medical authority in the activity of diagnosis by delivering their own diagnostic judgments, resisting clinicians' diagnoses, and orienting to clinicians' accounts for their diagnostic statements and reasoning, thus displaying companion agency in the Chinese outpatient clinical decision-making and indicating a transition from a paternalistic model to a family-centered model of the doctor-patient relationship in the Chinese orthopedic outpatient interaction. This study furthers current knowledge of companion involvement across healthcare contexts and contributes to raising clinicians' awareness of the significance of companions’ contributions in Chinese outpatient clinical interaction.

Reference Values for Videofluoroscopic Measures of Swallowing: An Update.

It is essential that clinicians have evidence-based benchmarks to support accurate diagnosis and clinical decision making. Recent studies report poor reliability for diagnostic judgments and identifying mechanisms of impairment from videofluoroscopy (VFSS). Establishing VFSS reference values for healthy swallowing would help resolve such discrepancies. Steele et al. (2019) released preliminary reference data for quantitative VFSS measures in healthy adults aged < 60 years. Here, we extend that work to provide reference percentiles for VFSS measures across a larger age span. Data for 16 VFSS parameters were collected from 78 healthy adults aged 21-82 years (39 male). Participants swallowed three comfortable sips each of thin, slightly, mildly, moderately, and extremely thick barium (20% w/v). VFSS recordings were analyzed in duplicate by trained raters, blind to participant and task, using the Analysis of Swallowing Physiology: Events, Kinematics and Timing (ASPEKT) Method. Reference percentiles (p2.5, 5, 25, 50, 75, 95, and 97.5) were determined as per Clinical and Laboratory Standards Institute EP28-A3c guidelines. We present VFSS reference percentile tables, by consistency, for (a) timing parameters (swallow reaction time; the hyoid burst-to-upper esophageal sphincter (UES)-opening interval; UES opening duration; time-to-laryngeal vestibule closure (LVC); and LVC duration) and (b) anatomically scaled pixel-based measures of maximum UES diameter, pharyngeal area at maximum pharyngeal constriction and rest, residue (vallecular, pyriform, other pharyngeal locations, total), and hyoid kinematics (X, Y, XY coordinates of peak position; speed). Clinical decision limits are proposed to demarcate atypical values of potential clinical concern. These updated reference percentiles and proposed clinical decision limits are intended to support interpretation and reliability for VFSS assessment data. https://doi.org/10.23641/asha.24043041.

Rejection! Or is it? Correlation among molecular microscope diagnostic system, histopathology and clinical judgement following heart transplantation

PurposeLittle is known about clinical decision making among discordant findings concerning for rejection with endomyocardial biopsy (EMBx) and Molecular Microscope Diagnostic System (MMDx) in patients following heart transplantation. MethodsTwo hundred and twenty-eight corresponding EMBx and MMDx specimens from 135 adult heart transplant patients were retrospectively reviewed. Rejection was classified as t-cell mediated rejection ≥2R and/or antibody mediated rejection ≥1. Clinical decision making among concordant and discordant cases of EMBx and MMDx results were reviewed. ResultsPatient characteristics were comparable between concordant and discordant patient groups (median age 60 yrs., 76% male, and 71% White). A total of 167/228 specimens (73%) were concordant for no rejection with 98% agreement in clinical decision making and 25/228 (11%) concordant for rejection with 64% agreement in clinical decision making. Among the 36/228 (16%) discordant samples, clinical decision-making agreed on treatment for rejection in five of the MMDx samples and three of the EMBx samples. ConclusionsMMDx can be an additional tool to diagnose rejection not detected by the traditional EMBx and influence clinical decision making in guiding appropriate treatment. Ongoing investigation into the clinical utility of MMDx is warranted to determine the significance of discordant findings among diagnostic modalities when assessing for rejection.

Race and the Mental Representation of Individuals Diagnosed with Oppositional Defiant Disorder: Implications for Diagnosis

Introduction: This research investigates the possible role of racial and gender stereotypes in diagnosing children with Oppositional Defiant Disorder (ODD). ODD is diagnosed more readily in boys and Black children, although the factors producing differential diagnosis rates are unclear. The authors conducted six studies investigating the possibility that overlap between racial and gender stereotypes with ODD diagnostic criteria might contribute to gaps in its judged prevalence across groups. Method: Participants completed reverse correlation procedures to determine whether mental representations of children expected versus unexpected to be diagnosed with ODD differed in facial characteristics. Separate participants viewed these images and judged the likelihood that each person depicted had been diagnosed with ODD. Results: Classification images (CIs) showed that the children selected as having ODD appeared more prototypically Black in facial appearance than children not chosen as having ODD. No differences emerged in the gendered appearance of the two group-level CIs. Judged rates of ODD were higher for the children who appeared to be Black. However, diagnostic judgments of clinical trainees and practitioners were unaffected by appearance factors, suggesting that formal clinical training might attenuate the influence of stereotypes on judgment. Discussion: These results indicate that an overlap in Black stereotypes and diagnostic criteria for ODD might contribute to elevated diagnosis of ODD in African American children.

Философско-базисная модель психологии личности в пенитенциарной практике

Basic psychology and philosophy of personality is a new psychological and philosophical direction, which is based on the basic design of information about personality. Design is the modeling, construction and transformation of information. A basis is a set of elements having "completeness", "order" and "measure". The elements can be factors, types, functions, etc. The factor basis is a dichotomous (E) symmetric (C) unity (E) of factors. The basis is a function (D, C, E), i.e. a function of three variables (from three arguments - factors). Basic design is the modeling, construction and transformation of information in accordance with complete, ordered and measurable information. Let's build a three-dimensional basic design in the field of science functions (cognitive, ideological, systematizing, industrial, social, cultural, informational). All these functions are realized in the scientific activity of the individual.&#x0D; We have three three-dimensional bases (1), (2), (3). (1) Image, word, number (here the "substrate" will be "operands of thinking" - "reflection of relationships"), (2) Factors, diagnostics, evaluation (here the "substrate" will be "psychodiagnostic concepts" - "regulation of mappings"), (3) Completeness, order, measure (here the "substrate" will be "criteria-characteristics" from the definition of the basis – "transformation implementation"). By the way, a factor - dichotomous three-dimensional "completeness" appears here: F(1) (+,-), F(2) (+,-), F(3(+,-).&#x0D; Based on the definition of the main functions of science, it is possible to make triads of diagnostic judgments for each of the six types of functions (3x6 = 18) and thereby construct a basic three-dimensional test–18, consisting of 18 diagnostic judgments and measuring the profile - hexagram of individual preference for specific functions in the scientific activity of the individual. The creation of such a basic three-dimensional test is connected with the third criterion of "measure", which is included in the definition of "basis". It is possible to use "constructive" and "criterion" validity for the future new test-18. We will not do this in line with this article and this task is waiting for its researcher!

Collective intelligence improves probabilistic diagnostic assessments.

Collective intelligence, the "wisdom of the crowd," seeks to improve the quality of judgments by aggregating multiple individual inputs. Here, we evaluate the success of collective intelligence strategies applied to probabilistic diagnostic judgments. We compared the performance of individual and collective intelligence judgments on two series of clinical cases requiring probabilistic diagnostic assessments, or "forecasts". We assessed the quality of forecasts using Brier scores, which compare forecasts to observed outcomes. On both sets of cases, the collective intelligence answers outperformed nearly every individual forecaster or team. The improved performance by collective intelligence was mediated by both improved resolution and calibration of probabilistic assessments. In a secondary analysis looking at the effect of varying number of individual inputs in collective intelligence answers from two different data sources, nearly identical curves were found in the two data sets showing 11-12% improvement when averaging two independent inputs, 15% improvement averaging four independent inputs, and small incremental improvements with further increases in number of individual inputs. Our results suggest that the application of collective intelligence strategies to probabilistic diagnostic forecasts is a promising approach to improve diagnostic accuracy and reduce diagnostic error.

Multiparametric Analysis of Dysphonic Voice - An Evidence from the Discriminant Analysis.

Even though earlier studies have investigated the relationship between various subjective and instrumental measures of voice, determining a standardized set of voice parameters in evaluating dysphonic voices can help in better diagnostic distinctions and judgment of the treatment outcomes in voice disorders. Thus, the primary objective was to examine the differences in the objective and subjective measures of voice between the participants with dysphonia and participants with a clinically normal voice. The subsequent objective was to identify the group of parameters sensitive to vocal changes in dysphonia using discriminant analysis. Two groups of participants were included in the study. Group 1 comprised of 15 participants with dysphonia. Group 2 included 15 participants with a clinically normal voice. Sustained phonations of vowels were recorded from the participants of both groups and were analyzed perceptually using the GRBAS rating scale. Acoustic, cepstral, spectral, and electroglottographic measures were analyzed from dysphonic voices and normal controls. There were significant differences in both instrumental and perceptual measures between the participants with and without dysphonia. The set of five parameters that were significant predictors that discriminated the dysphonic voice from the clinically normal voice with 100% accuracy was also determined using discriminant analysis. Future investigations on the relation between the specific instrumental and perceptual measures of voice identified in the present study among individuals with various voice disorders can deliver more promising and comprehendible insights into better diagnostic distinctions of voice disorders.

Uncertainty, Evidence, and the Integration of Machine Learning into Medical Practice.

In light of recent advances in machine learning for medical applications, the automation of medical diagnostics is imminent. That said, before machine learning algorithms find their way into clinical practice, various problems at the epistemic level need to be overcome. In this paper, we discuss different sources of uncertainty arising for clinicians trying to evaluate the trustworthiness of algorithmic evidence when making diagnostic judgments. Thereby, we examine many of the limitations of current machine learning algorithms (with deep learning in particular) and highlight their relevance for medical diagnostics. Among the problems we inspect are the theoretical foundations of deep learning (which are not yet adequately understood), the opacity of algorithmic decisions, and the vulnerabilities of machine learning models, as well as concerns regarding the quality of medical data used to train the models. Building on this, we discuss different desiderata for an uncertainty amelioration strategy that ensures that the integration of machine learning into clinical settings proves to be medically beneficial in a meaningful way.