Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients’ attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients’ perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists’ expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics.

Background. The use of transcranial direct current stimulation (tDCS) to modulate pain, psychological aspects, and cognitive functions has increased in recent years. The present scoping review aims to investigate the use of tDCS in cancer patients and its significant impact on psychocognitive and pain related symptoms. Methods. From the earliest available date to June 2023, a comprehensive search was conducted in three electronic scientific databasesPubMed, Scopus, and Embaseand other supplementary sources. Ten relevant studies were identified and included, comprising single case studies, randomized controlled trials, pilot studies, and one retrospective study. PRISMA guidelines for scoping reviews were followed. Results. These studies investigated the use of tDCS to improve pain and psychocognitive aspects in patients with various types of cancer, including breast, oral, bladder, lung, pancreatic, head and neck cancer, hepatocellular carcinoma, and meningioma. Overall, the results suggest that tDCS has shown efficacy in relieving pain, reducing anxiety and depression, and improving cognitive function in cancer patients. Conclusion. Due to the limited number and high heterogeneity of the existing literature in this field, more investigation and the establishment of standardized protocols would be required to obtain more conclusive evidence.

Health literacy is essential in cancer care. Low health literacy compromises the capacity to maintain one's health through self-management and collaboration with healthcare providers, especially when facing cognitive side effects related to cancer and its treatments. Cancer-related cognitive impairment (CRCI) is a common phenomenon among cancer patients and might determine a significant impact on their quality of life, yet it is still under identified by both clinicians and patients. This perspective aims to discuss the implications of patients' and healthcare professionals' lack of awareness on the matter and argues about the importance of improving the level of information on CRCI to mitigate difficulties in identifying and managing such manifestations on various levels.

Background: Cancer-related cognitive impairment (CRCI) is a significant concern for individuals with non-central nervous system (non-CNS) cancers, affecting memory, attention, executive functions, and processing speed. Non-pharmacological interventions, including digital cognitive rehabilitation, have shown promise in addressing CRCI. This systematic review investigates the efficacy of digital and computerized cognitive rehabilitation interventions in improving cognitive outcomes in non-CNS cancer patients. Method: A systematic search of the EMBASE, Scopus, and PubMed databases was conducted to identify studies on digital and computerized cognitive rehabilitation for non-CNS cancer patients. Studies were included if they involved computerized and digital cognitive rehabilitation for oncological patients and assessed the efficacy of the intervention. A total of 11 studies were selected, including randomized controlled trials and quasi-experimental designs. The quality of the studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Data was synthesized using a narrative descriptive approach, and the results were summarized in a descriptive table. Results: The most frequently assessed cognitive domains included working memory, attention, executive functions, and processing speed. The majority of studies (n = 11) demonstrated significant immediate improvements in cognitive functions, particularly in working memory, executive functions, attention, and processing speed. Short-term follow-up (1–5 months) showed partial maintenance of these improvements, while long-term effects (6 months to 1 year) were more variable. Improvements in episodic memory were less consistent, particularly among breast cancer survivors. Discussion: Digital and computerized cognitive rehabilitation appears to be an effective intervention for CRCI, providing immediate cognitive benefits and some lasting improvements, especially in domains such as memory and attention. However, long-term effects remain variable, and further research is needed to explore the optimal duration of interventions and the potential advantages of personalized rehabilitation approaches.

Artificial intelligence (AI) is increasingly integrated into breast imaging workflows, offering the potential to enhance diagnostic accuracy, efficiency, and early cancer detection. Image interpretation plays a pivotal role in the breast cancer diagnostic pathway, directly influencing therapeutic decisions and patient outcomes. However, the effective implementation of AI-assisted systems relies not only on technical performance but also on radiologists’ trust, acceptance, and readiness to incorporate these tools into clinical practice. In addition, system-related, perceptual, and cognitive factors may contribute to diagnostic errors, ultimately affecting overall accuracy and reliability. This paper provides a comprehensive overview of the cognitive and systemic sources of diagnostic inaccuracies in breast imaging, emphasizing the growing role of AI as both a supportive and potentially bias-modulating tool. Recent prospective studies have demonstrated the clinical safety and effectiveness of AI-assisted mammography screening, reporting improved cancer detection rates and reduced workload. Nonetheless, the integration of AI into diagnostic workflows without an appropriate knowledge of the consequences may introduce new cognitive biases, such as anchoring, automation, and confirmation bias, that influence radiologists’ decision-making and counteract the intended benefits. To address these challenges, the paper outlines strategies to mitigate diagnostic errors and foster appropriate integration of AI into clinical practice. These include targeted training programs, enhanced interdisciplinary communication, and standardized interpretation workflows that promote consistent evidence-based practice. Furthermore, the adoption of explainable AI approaches is identified as a key factor in improving model transparency and interpretability, allowing radiologists to understand algorithmic reasoning and engage in a more informed, confidence-based human-AI collaboration. Ultimately, a balanced and context-sensitive integration of AI, grounded in continuous professional education and cognitive awareness, is essential for improving diagnostic accuracy while preserving radiologists’ critical analytical skills.