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Background: The frequency of hip and knee arthroplasty surgeries has been rising steadily in recent decades. This trend is attributed to an aging population, leading to increased demands on healthcare systems. Fast Track (FT) surgical protocols, perioperative procedures designed to expedite patient recovery and early mobilization, have demonstrated efficacy in reducing hospital stays, convalescence periods, and associated costs. However, the criteria for selecting patients for FT procedures have not fully capitalized on the available patient data, including patient-reported outcome measures (PROMs). Methods: Our study focused on developing machine learning (ML) models to support decision making in assigning patients to FT procedures, utilizing data from patients' self-reported health status. These models are specifically designed to predict the potential health status improvement in patients initially selected for FT. Our approach focused on techniques inspired by the concept of controllable AI. This includes eXplainable AI (XAI), which aims to make the model's recommendations comprehensible to clinicians, and cautious prediction, a method used to alert clinicians about potential control losses, thereby enhancing the models' trustworthiness and reliability. Results: Our models were trained and tested using a dataset comprising 899 records from individual patients admitted to the FT program at IRCCS Ospedale Galeazzi-Sant'Ambrogio. After training and selecting hyper-parameters, the models were assessed using a separate internal test set. The interpretable models demonstrated performance on par or even better than the most effective 'black-box' model (Random Forest). These models achieved sensitivity, specificity, and positive predictive value (PPV) exceeding 70%, with an area under the curve (AUC) greater than 80%. The cautious prediction models exhibited enhanced performance while maintaining satisfactory coverage (over 50%). Further, when externally validated on a

In this article, we survey the applications of Three-way decision theory (TWD) in machine learning (ML), focusing in particular on four tasks: weakly supervised learning and multi-source data management, missing data management, uncertainty quantification in classification, and uncertainty quantification in clustering. For each of these four tasks we present the results of a systematic review of the literature, by which we report on the main characteristics of the current state of the art, as well as on the quality of reporting and reproducibility level of the works found in the literature. To this aim, we discuss the main benefits, limitations and issues found in the reviewed articles, and we give clear indications and directions for quality improvement that are informed by validation, reporting, and reproducibility standards, guidelines and best practice that have recently emerged in the ML field. Finally, we discuss about the more promising and relevant directions for future research in regard to TWD.

This study explores the concept of similarity in machine learning (ML) and its congruence with human judgment in medical contexts, focusing primarily on radiology. We conducted a user study involving two radiologists and two orthopedic and spine surgeons. These experts evaluated the similarity of 72 cases, selected from a larger dataset by an ML model based on Cosine and Euclidean distances, in comparison to 18 representative base cases of vertebral fractures. Our analysis focused on correlating these ML-derived distances with the experts’ assessments. The findings reveal that: (1) both Cosine and Euclidean distances had limited correlation with human judgments; (2) Cosine distances showed a marginally higher correlation than Euclidean distances; despite the limitations due to the small samples of evaluations and evaluators, our findings emphasize the necessity for ongoing research to enhance AI similarity metrics, aiming for greater human-centricity and relevance, particularly considering their critical role in ML training and inference. Our study’s implications are far-reaching, advocating for a comprehensive reevaluation of similarity assessments in AI to achieve a closer alignment with human cognitive processes, extending well beyond the realm of medical imaging.

In this article we study the problem of learning from fuzzy labels (LFL), a form of weakly supervised learning in which the supervision target is not precisely specified but is instead given in the form of possibility distributions, that express the imprecise knowledge of the annotating agent. While several approaches for LFL have been proposed in the literature, including generalized risk minimization (GRM), instance-based methods and pseudo label-based learning, both their theoretical properties and their empirical performance have scarcely been studied. We address this gap by: first, presenting a review of the previous results relative to the sample complexity and generalization bounds for GRM and instance-based methods; second, studying both their computational complexity, by proving in particular the impossibility of efficiently solving LFL using GRM, as well as impossibility theorems. We then propose a novel pseudo label-based learning method, called Random Resampling-based Learning (RRL), which directly draws from ensemble learning and possibility theory and study its learning- and complexity-theoretic properties, showing that it achieves guarantees similar to those for GRM while being computationally efficient. Finally, we study the empirical performance of several state-of-the-art LFL algorithms on wide set of synthetic and real-world benchmark datasets, by which we confirm the effectiveness of the proposed RRL method. Additionally, we describe directions for future research, and highlight opportunities for further interaction between machine learning and uncertainty representation theories.

In this article we propose a conceptual framework to study ensembles of conformal predictors (CP), that we call Ensemble Predictors (EP). Our approach is inspired by the application of imprecise probabilities in information fusion. Based on the proposed framework, we study, for the first time in the literature, the theoretical properties of CP ensembles in a general setting, by focusing on simple and commonly used possibilistic combination rules. We also illustrate the applicability of the proposed methods in the setting of multivariate time-series classification, showing that these methods provide better performance (in terms of both robustness, conservativeness, accuracy and running time) than both standard classification algorithms and other combination rules proposed in the literature, on a large set of benchmarks from the UCR time series archive.

This paper proposes a user study aimed at evaluating the impact of Class Activation Maps (CAMs) as an eXplainable AI (XAI) method in a radiological diagnostic task, the detection of thoracolumbar (TL) fractures from vertebral X-rays. In particular, we focus on two oft-neglected features of CAMs, that is granularity and coloring, in terms of what features, lower-level vs higher-level, should the maps highlight and adopting which coloring scheme, to bring better impact to the decision-making process, both in terms of diagnostic accuracy (that is effectiveness) and of user-centered dimensions, such as perceived confidence and utility (that is satisfaction), depending on case complexity, AI accuracy, and user expertise. Our findings show that lower-level features CAMs, which highlight more focused anatomical landmarks, are associated with higher diagnostic accuracy than higher-level features CAMs, particularly among experienced physicians. Moreover, despite the intuitive appeal of semantic CAMs, traditionally colored CAMs consistently yielded higher diagnostic accuracy across all groups. Our results challenge some prevalent assumptions in the XAI field and emphasize the importance of adopting an evidence-based and human-centered approach to design and evaluate AI- and XAI-assisted diagnostic tools. To this aim, the paper also proposes a hierarchy of evidence framework to help designers and practitioners choose the XAI solutions that optimize performance and satisfaction on the basis of the strongest evidence available or to focus on the gaps in the literature that need to be filled to move from opinionated and eminence-based research to one more based on empirical evidence and end-user work and preferences.

This paper examines a kind of explainable AI, centered around what we term pro-hoc explanations, that is a form of support that consists of offering alternative explanations (one for each possible outcome) instead of a specific post-hoc explanation following specific advice. Specifically, our support mechanism utilizes explanations by examples, featuring analogous cases for each category in a binary setting. Pro-hoc explanations are an instance of what we called frictional AI, a general class of decision support aimed at achieving a useful compromise between the increase of decision effectiveness and the mitigation of cognitive risks, such as over-reliance, automation bias and deskilling. To illustrate an instance of frictional AI, we conducted an empirical user study to investigate its impact on the task of radiological detection of vertebral fractures in x-rays. Our study engaged 16 orthopedists in a ‘human-first, second-opinion’ interaction protocol. In this protocol, clinicians first made initial assessments of the x-rays without AI assistance and then provided their final diagnosis after considering the pro-hoc explanations. Our findings indicate that physicians, particularly those with less experience, perceived pro-hoc XAI support as significantly beneficial, even though it did not notably enhance their diagnostic accuracy. However, their increased confidence in final diagnoses suggests a positive overall impact. Given the promisingly high effect size observed, our results advocate for further research into pro-hoc explanations specifically, and into the broader concept of frictional AI.

We introduce partially-defined equivalence relations as a type of equivalence relation that incorporates uncertainty. In these relations, certain pairs of objects are not definitively determined to be related or unrelated. The relationship with orthopartitions is put forward, providing the conditions under which an orthopartition can be transformed into an equivalent partially-defined equivalence relation and vice versa. Additionally, we explore their connection with reducts in rough set theory, offering insights into the characterization of similarity reducts in terms of orthopartitions.

Artificial Intelligence (AI) use in automated Electrocardiogram (ECG) classification has continuously attracted the research community's interest, motivated by their promising results. Despite their great promise, limited attention has been paid to the robustness of their results, which is a key element for their implementation in clinical practice. Uncertainty Quantification (UQ) is a critical for trustworthy and reliable AI, particularly in safety-critical domains such as medicine. Estimating uncertainty in Machine Learning (ML) model predictions has been extensively used for Out-of-Distribution (OOD) detection under single-label tasks. However, the use of UQ methods in multi-label classification remains underexplored. This study goes beyond developing highly accurate models comparing five uncertainty quantification methods using the same Deep Neural Network (DNN) architecture across various validation scenarios, including internal and external validation as well as OOD detection, taking multi-label ECG classification as the example domain. We show the importance of external validation and its impact on classification performance, uncertainty estimates quality, and calibration. Ensemble-based methods yield more robust uncertainty estimations than single network or stochastic methods. Although current methods still have limitations in accurately quantifying uncertainty, particularly in the case of dataset shift, incorporating uncertainty estimates with a classification with a rejection option improves the ability to detect such changes. Moreover, we show that using uncertainty estimates as a criterion for sample selection in active learning setting results in greater improvements in classification performance compared to random sampling.

In this article, we propose a conceptual and methodological framework for measuring the impact of the introduction of AI systems in decision settings, based on the concept of technological dominance, i.e. the influence that an AI system can exert on human judgment and decisions. We distinguish between a negative component of dominance (automation bias) and a positive one (algorithm appreciation) by focusing on and systematizing the patterns of interaction between human judgment and AI support, or reliance patterns, and their associated cognitive effects. We then define statistical approaches for measuring these dimensions of dominance, as well as corresponding qualitative visualizations. By reporting about four medical case studies, we illustrate how the proposed methods can be used to inform assessments of dominance and of related cognitive biases in real-world settings. Our study lays the groundwork for future investigations into the effects of introducing AI support into naturalistic and collaborative decision-making.

Dementia refers to a group of neurodegenerative disorders that impact the cognitive function of an increasing number of individuals. Because of the variety of manifestations, the idea of mixed dementia has recently garnered increased awareness and attention from the scientific community. In this work, we describe a high-quality dataset, as well as the findings of a preliminary analysis devoted to investigating the potential of computational methods that are highly indicative of mixed dementia. We will specifically describe the findings of a phenotypic stratification analysis, based on clustering approaches, that highlights possibly significant aspects of mixed dementia, paving the way for further research devoted to the application of Machine Learning techniques to the robust and early diagnosis of mixed dementia.

This paper discusses the results of an exploratory study aimed at investigating the impact of conversational agents (CAs) and specifically their agential characteristics on collaborative decision-making processes. The study involved 29 participants divided into 8 small teams engaged in a question-and-answer trivia-style game with the support of a text-based CA, characterized by two independent binary variables: personality (gentle and cooperative vs blunt and uncooperative) and gender (female vs male). A semi-structured group interview was conducted at the end of the experimental sessions to investigate the perceived utility and level of satisfaction with the CAs. Our results show that when users interact with a gentle and cooperative CA, their user satisfaction is higher. Furthermore, female CAs are perceived as more useful and satisfying to interact with than male CAs. We show that group performance improves through interaction with the CAs, confirming that a stereotype favoring the female with a gentle and cooperative personality combination exists in regard to perceived satisfaction, even though this does not lead to greater perceived utility. Our study extends the current debate about the possible correlation between CA characteristics and human acceptance and suggests future research to investigate the role of gender bias and related biases in human-AI teaming.