In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

Interest in Machine Learning applications to tackle clinical and biological problems is increasing. This is driven by promising results reported in many research papers, the increasing number of AI-based software products, and by the general interest in Artificial Intelligence to solve complex problems. It is therefore of importance to improve the quality of machine learning output and add safeguards to support their adoption. In addition to regulatory and logistical strategies, a crucial aspect is to detect when a Machine Learning model is not able to generalize to new unseen instances, which may originate from a population distant to that of the training population or from an under-represented subpopulation. As a result, the prediction of the machine learning model for these instances may be often wrong, given that the model is applied outside its "reliable" space of work, leading to a decreasing trust of the final users, such as clinicians. For this reason, when a model is deployed in practice, it would be important to advise users when the model's predictions may be unreliable, especially in high-stakes applications, including those in healthcare. Yet, reliability assessment of each machine learning prediction is still poorly addressed. Here, we review approaches that can support the identification of unreliable predictions, we harmonize the notation and terminology of relevant concepts, and we highlight and extend possible interrelationships and overlap among concepts. We then demonstrate, on simulated and real data for ICU in-hospital death prediction, a possible integrative framework for the identification of reliable and unreliable predictions. To do so, our proposed approach implements two complementary principles, namely the density principle and the local fit principle. The density principle verifies that the instance we want to evaluate is similar to the training set. The local fit principle verifies that the trained model performs well on training subsets that are more similar to the instance under evaluation. Our work can contribute to consolidating work in machine learning especially in medicine.

Robotic technology is expected to transform rehabilitation settings, by providing precise, repetitive, and task-specific interventions, thereby potentially improving patients’ clinical outcomes. Artificial intelligence (AI) and machine learning (ML) have been widely applied in different areas to support robotic rehabilitation, from controlling robot movements to real-time patient assessment. To provide an overview of the current landscape and the impact of AI/ML use in robotics rehabilitation, we performed a systematic review focusing on the use of AI and robotics in rehabilitation from a broad perspective, encompassing different pathologies and body districts, and considering both motor and neurocognitive rehabilitation. We searched the Scopus and IEEE Xplore databases, focusing on the studies involving human participants. After article retrieval, a tagging phase was carried out to devise a comprehensive and easily-interpretable taxonomy: its categories include the aim of the AI/ML within the rehabilitation system, the type of algorithms used, and the location of robots and sensors. The 201 selected articles span multiple domains and diverse aims, such as movement classification, trajectory prediction, and patient evaluation, demonstrating the potential of ML to revolutionize personalized therapy and improve patient engagement. ML is reported as highly effective in predicting movement intentions, assessing clinical outcomes, and detecting compensatory movements, providing insights into the future of personalized rehabilitation interventions. Our analysis also reveals pitfalls in the current use of AI/ML in this area, such as potential explainability issues and poor generalization ability when these systems are applied in real-world settings.

Background: This study aims to address the gap in understanding clinicians’ attitudes toward explainable AI (XAI) methods applied to machine learning models using tabular data, commonly found in clinical settings. It specifically explores clinicians’ perceptions of different XAI methods from the ALFABETO project, which predicts COVID-19 patient hospitalization based on clinical, laboratory, and chest X-ray at time of presentation to the Emergency Department. The focus is on two cognitive dimensions: understandability and actionability of the explanations provided by explainable-by-design and post-hoc methods. Methods: A questionnaire-based experiment was conducted with 10 clinicians from the IRCCS Policlinico San Matteo Foundation in Pavia, Italy. Each clinician evaluated 10 real-world cases, rating predictions and explanations from three XAI tools: Bayesian networks, SHapley Additive exPlanations (SHAP), and AraucanaXAI. Two cognitive statements for each method were rated on a Likert scale, as well as the agreement with the prediction. Two clinicians answered the survey during think-aloud interviews. Results: Clinicians demonstrated generally positive attitudes toward AI, but high compliance rates (86% on average) indicate a risk of automation bias. Understandability and actionability are positively correlated, with SHAP being the preferred method due to its simplicity. However, the perception of methods varies according to specialty and expertise. Conclusions: The findings suggest that SHAP and AraucanaXAI are promising candidates for improving the use of XAI in clinical decision support systems (DSSs), highlighting the importance of clinicians’ expertise, specialty, and setting on the selection and development of supportive XAI advice. Finally, the study provides valuable insights into the design of future XAI DSSs.

In recent years, numerous robotic devices, together with allied technologies, have been developed to support rehabilitation, both in research settings and industry. Although robotic-assisted rehabilitation and related technologies hold significant promise for supporting healthcare practitioners and enhancing patient care, their use in clinical practice remains limited. One of the motivations could be that final users’ needs have not been given due consideration so far. As a matter of fact, understanding user needs and perceptions is crucial for designing these technological devices and for creating new organizational models within hospitals aiming to establish and maintain robotics-assisted rehabilitation gyms. We developed and distributed an online survey to the Italian community of healthcare practitioners working in rehabilitation, to depict the current landscape of robotic-assisted rehabilitation and to understand their opinions and demands across various domains and diseases. The questionnaire is divided into two main parts. The first section pertains to the respondents' demographics and professional experience. The second part includes questions about eight different categories of rehabilitative devices. For each category, practitioners can indicate whether they use a device in their practice, their perceptions, and any perceived barriers. Additionally, they can fill out a System Usability Scale for a specific device in that category. We collected answers from 423 Italian rehabilitation professionals, including various clinical roles, that revealed significant insights into the use of robotics in rehabilitation. Gender distribution shows a high prevalence of female professionals. 40% of respondents reported being unfamiliar with any robotics devices. Advanced treadmills are the most known and used robots. Generally, usage and experience with devices are associated with positive attitudes towards robotics-assisted rehabilitation. Lack of financial resources and scientific evidence, as well as lack of opportunities and training, are the most reported barriers. Despite a general positivity towards technology, there is a substantial lack of awareness about rehabilitation devices among professionals. The survey highlights the need for enhanced training and education on robotics in rehabilitation programs. Additionally, the limited focus on home rehabilitation is noted. The study emphasizes the importance of verifying both the effectiveness and economic sustainability of robotic devices in clinical practice.