Individuals with cancer and their families assume responsibility for management of cancer as an acute and chronic disease. Yet, cancer lags other chronic diseases in its provision of proactive self-management support in routine, everyday care leaving this population vulnerable to worse health status, long-term disability, and poorer survival. Enabling cancer patients to manage the medical and emotional consequences and lifestyle and work changes due to cancer and treatment is essential to optimizing health and recovery across the continuum of cancer. In this paper, the Global Partners on Self-Management in Cancer puts forth six priority areas for action: Action 1: Prepare patients and survivors for active involvement in care; Action 2: Shift the care culture to support patients as partners in cocreating health and embed self-management support in everyday health-care provider practices and in care pathways; Action 3: Prepare the workforce in the knowledge and skills necessary to enable patients in effective self-management and reach consensus on core curricula; Action 4: Establish and reach consensus on a patient-reported outcome system for measuring the effects of self-management support and performance accountability; Action 5: Advance the evidence and stimulate research on self-management and self-management support in cancer populations; Action 6: Expand reach and access to self-management support programs across care sectors and tailored to diversity of need and stimulation of research to advance knowledge. It is time for a revolution to better integrate self-management support as part of high-quality, person-centered support and precision medicine in cancer care to optimize health outcomes, accelerate recovery, and possibly improve survival.

This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box algorithms. While machine learning algorithms are increasingly employed for medical analysis and diagnosis, their complexity underscores the importance of understanding how these algorithms explain and interpret data to take informed decisions. This review comprehensively analyzes challenges and solutions presented in the literature, offering an overview of the most recent techniques utilized in this field. It also provides precise definitions of interpretability and explainability, aiming to clarify the distinctions between these concepts and their implications for the decision-making process. Our analysis, based on 448 articles and addressing seven research questions, reveals an exponential growth in this field over the last decade. The psychological dimensions of public perception underscore the necessity for effective communication regarding the capabilities and limitations of artificial intelligence. Researchers are actively developing techniques to enhance interpretability, employing visualization methods and reducing model complexity. However, the persistent challenge lies in finding the delicate balance between achieving high performance and maintaining interpretability. Acknowledging the growing significance of artificial intelligence in aiding medical diagnosis and therapy, and the creation of interpretable artificial intelligence models is considered essential. In this dynamic context, an unwavering commitment to transparency, ethical considerations, and interdisciplinary collaboration is imperative to ensure the responsible use of artificial intelligence. This collective commitment is vital for establishing enduring trust between clinicians and patients, addressing emerging challenges, and facilitating the informed adoption of these advanced technologies in medicine.

Objective Since its birth about 30 years ago, Narrative Medicine approach has increased in popularity in the medical context as well as in other disciplines. This paper aims to review Narrative Medicine research studies on patients' and their caregivers' illness experience. Setting and participants MEDLINE, Psycinfo, EBSCO Psychological and Behavioural Science, The Cochrane Library and CINAHL databases were searched to identify all the research studies which focused on the Narrative Medicine approach reported in the title, in the abstract and in the keywords the words ‘Narrative Medicine’ or ‘Narrative-based Medicine’. Primary and secondary outcome measures: number of participants, type of disease, race and age of participants, type of study, dependent variables, intervention methods, assessment. Results Of the 325 titles screened, we identified 10 research articles fitting the inclusion criteria. Our systematic review showed that research on Narrative Medicine has no common specific methodology: narrative in Medicine is used as an intervention protocol as well as an assessment tool. Patients' characteristics, types of disease and data analysis procedures differ among the screened studies. Conclusions Narrative Medicine research in medical practice needs to find clear and specific protocols to deepen the impact of narrative on medical practice and on patients' lives.