The increasing global burden of chronic kidney disease (CKD) necessitates an ongoing exploration of innovative strategies to enhance patient management and optimise healthcare delivery. Artificial Intelligence (AI) offers unprecedented opportunities for risk stratification in kidney care, harnessing vast datasets to inform clinical decision-making, improve patient outcomes, and drive efficiencies in healthcare systems.

AI-based CKD progression prediction models leverage machine learning algorithms to analyse patient data, enabling healthcare professionals to identify individuals at heightened risk of disease progression. These tools have been validated in various clinical settings and demonstrate considerable potential in personalising patient care.

Machine learning risk scoring for dialysis initiation has emerged as a vital resource, improving decision-making processes by facilitating timely interventions. Predictive analytics for transplant eligibility ensure that patients receive the most suitable and timely interventions based on their unique health profiles. This algorithmic stratification of renal patient cohorts allows for a more nuanced understanding of patient needs, moving us beyond a one-size-fits-all approach to a model that emphasises individualised care.

The potential of AI in risk stratification encompasses the broad spectrum of healthcare delivery, influencing policy formulation, resource allocation, and patient education. By continuing to expand our collective understanding in this area, we can foster an environment where AI-driven innovations are systematically integrated into kidney care protocols.

Topics of interest include but are not limited to:

• AI-based CKD progression prediction
• Machine learning risk scoring for dialysis initiation
• Predictive analytics for transplant eligibility
• Algorithmic stratification of renal patient cohorts
• Personalised nephrology risk models
• Integration of social determinants in risk assessment
• Impact of AI on patient engagement and outcomes
• Interoperability challenges and solutions in kidney care

We welcome submissions employing AI and Machine Learning approaches, as well prediction models that use regression or machine learning methods. We recommend adhering to TRIPOD guidelines for reporting clinical prediction models that use regression or machine learning methods.

We encourage authors to share accompanying data. According to our policies, submission of a manuscript to a BMC journal implies that materials described in the manuscript, including all relevant raw data, will be freely available to any scientist wishing to use them for non-commercial purposes, without breaching participant confidentiality. We require data deposition for some data types. Further information can be found in our policies.

This Collection supports and amplifies research related to SDG 3: Good Health and Well-being.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

The intersection of mental health and nephrology is gaining increasing recognition as a critical component of integrated patient care. Chronic kidney disease (CKD), along with treatments such as dialysis and kidney transplantation, is frequently associated with significant psychological distress, leading to poorer health outcomes. This Collection aims to explore the integration of mental health support within nephrology practices, addressing how psychological factors influence disease management and patient quality of life.

Recent advances in this field include the implementation of routine depression screening in dialysis settings and the development of integrated behavioral health models within nephrology clinics. These strategies have shown promise in addressing the psychological needs of patients and their family caregivers, fostering better adherence to treatment regimens, and ultimately improving physical and mental well-being among this population.

We welcome submissions including original research, reviews, and implementation studies. Topics of interest include, but are not limited to:

• Psychological support in CKD
• Depression screening in dialysis and transplant patients
• Integrated behavioral health in nephrology care
• Cognitive and behavioral interventions in CKD management
• Digital mental health interventions in nephrology
• Dyadic and family-based interventions in CKD management

This Collection supports and amplifies research related to SDG 3: Good Health and Well-being

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.