Artificial Intelligence-Based Clinical Decision Support Systems in Geriatrics: An Ethical Analysis.

OBJECTIVES: To provide an ethical analysis of the implications of the usage of artificial intelligence-supported clinical decision support systems (AI-CDSS) in geriatrics. DESIGN: Ethical analysis based on the normative arguments regarding the use of AI-CDSS in geriatrics using a principle-based ethical framework. SETTING AND PARTICIPANTS: Normative arguments identified in 29 articles on AI-CDSS in geriatrics. METHODS: Our analysis is based on a literature search that was done to determine ethical arguments that are currently discussed regarding AI-CDSS. The relevant articles were subjected to a detailed qualitative analysis regarding the ethical considerations Supplementary Datamentioned therein. We then discussed the identified arguments within the frame of the 4 principles of medical ethics according to Beauchamp and Childress and with respect to the needs of frail older adults. RESULTS: We found a total of 5089 articles; 29 articles met the inclusion criteria and were subsequently subjected to a detailed qualitative analysis. We could not identify any systematic analysis of the ethical implications of AI-CDSS in geriatrics. The ethical considerations are very unsystematic and scattered, and the existing literature has a predominantly technical focus emphasizing the technology's utility. In an extensive ethical analysis, we systematically discuss the ethical implications of the usage of AI-CDSS in geriatrics. CONCLUSIONS AND IMPLICATIONS: AI-CDSS in geriatrics can be a great asset, especially when dealing with patients with cognitive disorders; however, from an ethical perspective, we see the need for further research. By using AI-CDSS, older patients' values and beliefs might be overlooked, and the quality of the doctor-patient relationship might be altered, endangering compliance to the 4 ethical principles of Beauchamp and Childress.

Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician.

INTRODUCTION: Geriatric co-management is known to improve treatment of older adults in various clinical settings, however, widespread application of the concept is limited due to restricted resources. Digitalization may offer options to overcome these shortages by providing structured, relevant information and decision support tools for medical professionals. We present the SURGE-Ahead project (Supporting SURgery with GEriatric co-management and Artificial Intelligence) addressing this challenge. METHODS: A digital application with a dashboard-style user interface will be developed, displaying 1) evidence-based recommendations for geriatric co-management and 2) artificial intelligence-enhanced suggestions for continuity of care (COC) decisions. The development and implementation of the SURGE-Ahead application (SAA) will follow the Medical research council framework for complex medical interventions. In the development phase a minimum geriatric data set (MGDS) will be defined that combines parametrized information from the hospital information system with a concise assessment battery and sensor data. Two literature reviews will be conducted to create an evidence base for co-management and COC suggestions that will be used to display guideline-compliant recommendations. Principles of machine learning will be used for further data processing and COC proposals for the postoperative course. In an observational and AI-development study, data will be collected in three surgical departments of a University Hospital (trauma surgery, general and visceral surgery, urology) for AI-training, feasibility testing of the MGDS and identification of co-management needs. Usability will be tested in a workshop with potential users. During a subsequent project phase, the SAA will be tested and evaluated in clinical routine, allowing its further improvement through an iterative process. DISCUSSION: The outline offers insights into a novel and comprehensive project that combines geriatric co-management with digital support tools to improve inpatient surgical care and continuity of care of older adults. TRIAL REGISTRATION: German clinical trials registry (Deutsches Register für klinische Studien, DRKS00030684), registered on 21st November 2022.

Differential functional connectivity in thalamic and dopaminergic pathways in restless legs syndrome: a meta-analysis.

OBJECTIVE: Restless legs syndrome (RLS) is a sensorimotor disorder with alterations in somatosensory processing in association with a dysfunctional cerebral network, involving the basal ganglia, limbic network, and sensorimotor pathways. Resting state functional magnetic resonance imaging (MRI) is a powerful tool to provide in vivo insight into functional processing and as such is of special interest in RLS considering the widespread pattern of networks involved in this disorder. In this meta-analysis of resting state functional MRI studies, we analyzed the preponderance of functional connectivity changes associated with RLS and discussed possible links to sensorimotor dysfunction and somatosensory processing. METHODS: A systematic research using the online library PubMed was conducted and a total of seven studies passed the inclusion criteria of the meta-analysis. The results of these studies were merged and a statistical probability map was generated that indicated the likelihood of functional connectivity changes within the combined cohort, both for increased and decreased connectivity. RESULTS: The meta-analysis demonstrated decreased functional connectivity within the dopaminergic network in participants with RLS compared with healthy controls, including the nigrostriatal, mesolimbic, and mesocortical pathways. Increased functional connectivity was observed bilaterally in the thalamus, including its ventral lateral, ventral anterior, and ventral posterior lateral nuclei, and the pulvinar. DISCUSSION: Sensorimotor dysfunction in RLS seems to be reflected by decreased functional connectivity within the dopaminergic pathways. Network extension in the thalamus can be regarded as an adaptation to somatosensory dysfunction in RLS. This differential functional connectivity pattern extends prior findings on cerebral somatosensory processing in RLS and offers an explanation for the efficacy of dopaminergic treatment.