Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by “intelligent” machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments.

Background/Objectives: Diagnostic examinations that cutaneous melanoma (CM) patients undergo during follow-up can generate various emotional states related to the possibility of recurrence or disease progression. Understanding the emotions and perceptions of patients during the wait for diagnostic exams is crucial for improving care. This study aims to explore the experiences of CM patients awaiting follow-up diagnostic investigations. Methods: This is a qualitative descriptive inductive, single-center study, structured according to COREQ criteria. The sample of CM patients was recruited at the Radiology and Diagnostic Imaging Unit of the San Gallicano Dermatological Institute in Rome. Data were collected through semi-structured interviews and analyzed using Framework Analysis, as described by Ritchie and Spencer. Results: A total of 30 patients with an average age of 57.4 were recruited. The interviews revealed the following four themes: waiting influences the emotional experience; the double face of family support; strategies of support during the waiting period; and waiting requires attention to the person and the environment. Conclusions: This study revealed that waiting for a diagnostic exam for cutaneous melanoma (CM) is often characterized by anxiety and worry. Healthcare professionals need to implement positive coping strategies, balanced family support, and improved communication to enhance patient care and satisfaction in oncology settings. Recognizing and addressing patients’ emotional dynamics is crucial for improving care. Training healthcare staff, providing psychological interventions, and leveraging technological innovation can improve the waiting experience, foster a more welcoming and reassuring environment, and enhance patients’ psychological well-being and satisfaction with care pathways.

Background/Objectives: Diagnostic examinations that cutaneous melanoma (CM) patients undergo during follow-up can generate various emotional states related to the possibility of recurrence or disease progression. Understanding the emotions and perceptions of patients during the wait for diagnostic exams is crucial for improving care. This study aims to explore the experiences of CM patients awaiting follow-up diagnostic investigations. Methods: This is a qualitative descriptive inductive, single-center study, structured according to COREQ criteria. The sample of CM patients was recruited at the UOSD of Radiology and Diagnostic Imaging ISG of the IFO in Rome. Data was collected through semi-structured interviews and analyzed using Framework Analysis, as described by Ritchie and Spencer. Results: A total of 30 patients with an average age of 57.4 were recruited. The interviews revealed the following four themes: waiting influences the emotional experience; the double face of family support; strategies of support during the waiting period; waiting requires attention to the person and the environment. Conclusions: Healthcare professionals need to recognize and address patients' emotional dynamics to improve care. Training healthcare staff, psychological intervention, and technological innovation can improve the waiting experience, foster a more welcoming and reassuring environment, and enhance patients' psychological well-being and satisfaction with care pathways.