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Article ; Online: Conoscere bene l’intelligenza artificiale è il primo passo.

Gensini, Gian Franco / Cabitza, Federico

Giornale italiano di cardiologia (2006)

2022 Volume 23, Issue 10, Page(s) 771–772

Title translation	Getting to know artificial intelligence well is the first step.
MeSH term(s)	Algorithms ; Artificial Intelligence ; Humans
Language	Italian
Publishing date	2022-10-13
Publishing country	Italy
Document type	Journal Article
ZDB-ID	2272414-X
ISSN	1972-6481 ; 1827-6806
ISSN (online)	1972-6481
ISSN	1827-6806
DOI	10.1714/3881.38642
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Article: Editorial: Clinical Integration of Artificial Intelligence in Spine Surgery: Stepping in a new Frontier.

Gallazzi, Enrico / La Maida, Giovanni Andrea / Cabitza, Federico

Frontiers in surgery

2023 Volume 10, Page(s) 1351643

Language	English
Publishing date	2023-12-21
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2773823-1
ISSN	2296-875X
ISSN	2296-875X
DOI	10.3389/fsurg.2023.1351643
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Article ; Online: Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series Classification.

Campagner, Andrea / Barandas, Marilia / Folgado, Duarte / Gamboa, Hugo / Cabitza, Federico

IEEE transactions on pattern analysis and machine intelligence

2024 Volume PP

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2024-04-12
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2024.3388097
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Article ; Online: Decisions are not all equal-Introducing a utility metric based on case-wise raters' perceptions.

Campagner, Andrea / Sternini, Federico / Cabitza, Federico

Computer methods and programs in biomedicine

2022 Volume 221, Page(s) 106930

Abstract: Background and Objective Evaluation of AI-based decision support systems (AI-DSS) is of critical importance in practical applications, nonetheless common evaluation metrics fail to properly consider relevant and contextual information. In this article we ...

Abstract	Background and Objective Evaluation of AI-based decision support systems (AI-DSS) is of critical importance in practical applications, nonetheless common evaluation metrics fail to properly consider relevant and contextual information. In this article we discuss a novel utility metric, the weighted Utility (wU), for the evaluation of AI-DSS, which is based on the raters' perceptions of their annotation hesitation and of the relevance of the training cases. Methods We discuss the relationship between the proposed metric and other previous proposals; and we describe the application of the proposed metric for both model evaluation and optimization, through three realistic case studies. Results We show that our metric generalizes the well-known Net Benefit, as well as other common error-based and utility-based metrics. Through the empirical studies, we show that our metric can provide a more flexible tool for the evaluation of AI models. We also show that, compared to other optimization metrics, model optimization based on the wU can provide significantly better performance (AUC 0.862 vs 0.895, p-value <0.05), especially on cases judged to be more complex by the human annotators (AUC 0.85 vs 0.92, p-value <0.05). Conclusions We make the point for having utility as a primary concern in the evaluation and optimization of machine learning models in critical domains, like the medical one; and for the importance of a human-centred approach to assess the potential impact of AI models on human decision making also on the basis of further information that can be collected during the ground-truthing process.
MeSH term(s)	Benchmarking ; Humans ; Machine Learning
Language	English
Publishing date	2022-06-03
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2022.106930
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Article ; Online: The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies.

Cabitza, Federico / Campagner, Andrea

International journal of medical informatics

2021 Volume 153, Page(s) 104510

Abstract: This editorial aims to contribute to the current debate about the quality of studies that apply machine learning (ML) methodologies to medical data to extract value from them and provide clinicians with viable and useful tools supporting everyday care ... ...

Abstract	This editorial aims to contribute to the current debate about the quality of studies that apply machine learning (ML) methodologies to medical data to extract value from them and provide clinicians with viable and useful tools supporting everyday care practices. We propose a practical checklist to help authors to self assess the quality of their contribution and to help reviewers to recognize and appreciate high-quality medical ML studies by distinguishing them from the mere application of ML techniques to medical data.
MeSH term(s)	Artificial Intelligence ; Checklist ; Humans ; Machine Learning ; Medical Informatics
Language	English
Publishing date	2021-06-02
Publishing country	Ireland
Document type	Editorial
ZDB-ID	1466296-6
ISSN	1872-8243 ; 1386-5056
ISSN (online)	1872-8243
ISSN	1386-5056
DOI	10.1016/j.ijmedinf.2021.104510
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Article ; Online: Never tell me the odds: Investigating pro-hoc explanations in medical decision making.

Cabitza, Federico / Natali, Chiara / Famiglini, Lorenzo / Campagner, Andrea / Caccavella, Valerio / Gallazzi, Enrico

Artificial intelligence in medicine

2024 Volume 150, Page(s) 102819

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Humans ; Clinical Decision-Making ; Automation ; Physicians ; Radiology
Language	English
Publishing date	2024-03-01
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	645179-2
ISSN	1873-2860 ; 0933-3657
ISSN (online)	1873-2860
ISSN	0933-3657
DOI	10.1016/j.artmed.2024.102819
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Article ; Online: Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems.

Famiglini, Lorenzo / Campagner, Andrea / Barandas, Marilia / La Maida, Giovanni Andrea / Gallazzi, Enrico / Cabitza, Federico

Computers in biology and medicine

2024 Volume 170, Page(s) 108042

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Humans ; Mental Processes ; Radiology ; Semantics ; Spine
Language	English
Publishing date	2024-02-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2024.108042
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Article ; Online: Enhancing human-AI collaboration: The case of colonoscopy.

Introzzi, Luca / Zonca, Joshua / Cabitza, Federico / Cherubini, Paolo / Reverberi, Carlo

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver

2023

Abstract: Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by ... ...

Abstract	Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.
Language	English
Publishing date	2023-11-06
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1459373-7
ISSN	1878-3562 ; 1125-8055
ISSN (online)	1878-3562
ISSN	1125-8055
DOI	10.1016/j.dld.2023.10.018
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Article ; Online: Rising adoption of artificial intelligence in scientific publishing: evaluating the role, risks, and ethical implications in paper drafting and review process.

Carobene, Anna / Padoan, Andrea / Cabitza, Federico / Banfi, Giuseppe / Plebani, Mario

Clinical chemistry and laboratory medicine

2023 Volume 62, Issue 5, Page(s) 835–843

Abstract: Background: In the rapid evolving landscape of artificial intelligence (AI), scientific publishing is experiencing significant transformations. AI tools, while offering unparalleled efficiencies in paper drafting and peer review, also introduce notable ... ...

Abstract	Background: In the rapid evolving landscape of artificial intelligence (AI), scientific publishing is experiencing significant transformations. AI tools, while offering unparalleled efficiencies in paper drafting and peer review, also introduce notable ethical concerns. Content: This study delineates AI's dual role in scientific publishing: as a co-creator in the writing and review of scientific papers and as an ethical challenge. We first explore the potential of AI as an enhancer of efficiency, efficacy, and quality in creating scientific papers. A critical assessment follows, evaluating the risks vs. rewards for researchers, especially those early in their careers, emphasizing the need to maintain a balance between AI's capabilities and fostering independent reasoning and creativity. Subsequently, we delve into the ethical dilemmas of AI's involvement, particularly concerning originality, plagiarism, and preserving the genuine essence of scientific discourse. The evolving dynamics further highlight an overlooked aspect: the inadequate recognition of human reviewers in the academic community. With the increasing volume of scientific literature, tangible metrics and incentives for reviewers are proposed as essential to ensure a balanced academic environment. Summary: AI's incorporation in scientific publishing is promising yet comes with significant ethical and operational challenges. The role of human reviewers is accentuated, ensuring authenticity in an AI-influenced environment. Outlook: As the scientific community treads the path of AI integration, a balanced symbiosis between AI's efficiency and human discernment is pivotal. Emphasizing human expertise, while exploit artificial intelligence responsibly, will determine the trajectory of an ethically sound and efficient AI-augmented future in scientific publishing.
MeSH term(s)	Humans ; Artificial Intelligence ; Publishing ; Benchmarking ; Research Personnel
Language	English
Publishing date	2023-11-30
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1418007-8
ISSN	1437-4331 ; 1434-6621 ; 1437-8523
ISSN (online)	1437-4331
ISSN	1434-6621 ; 1437-8523
DOI	10.1515/cclm-2023-1136
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Article ; Online: A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients.

Famiglini, Lorenzo / Campagner, Andrea / Carobene, Anna / Cabitza, Federico

Medical & biological engineering & computing

2022

Abstract: In this article, we discuss the development of prognostic machine learning (ML) models for COVID-19 progression, by focusing on the task of predicting ICU admission within (any of) the next 5 days. On the basis of 6,625 complete blood count (CBC) tests ... ...

Abstract	In this article, we discuss the development of prognostic machine learning (ML) models for COVID-19 progression, by focusing on the task of predicting ICU admission within (any of) the next 5 days. On the basis of 6,625 complete blood count (CBC) tests from 1,004 patients, of which 18% were admitted to intensive care unit (ICU), we created four ML models, by adopting a robust development procedure which was designed to minimize risks of bias and over-fitting, according to reference guidelines. The best model, a support vector machine, had an AUC of .85, a Brier score of .14, and a standardized net benefit of .69: these scores indicate that the model performed well over a variety of prediction criteria. We also conducted an interpretability study to back up our findings, showing that the data on which the developed model is based is consistent with the current medical literature. This also demonstrates that CBC data and ML methods can be used to predict COVID-19 patients' ICU admission at a relatively low cost: in particular, since CBC data can be quickly obtained by means of routine blood exams, our models could be used in resource-constrained settings and provide health practitioners with rapid and reliable indications.
Language	English
Publishing date	2022-03-30
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	282327-5
ISSN	1741-0444 ; 0025-696X ; 0140-0118
ISSN (online)	1741-0444
ISSN	0025-696X ; 0140-0118
DOI	10.1007/s11517-022-02543-x
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