AI-Driven Personalization of End-of-Life Care for the Elderly

Launched by BAQIYATALLAH MEDICAL SCIENCES UNIVERSITY · Jun 11, 2025

Keywords

ClinConnect Summary

This clinical trial is exploring whether a special computer program using artificial intelligence (AI) can help improve care for elderly people who are in the final stages of their lives. The AI software works by monitoring a person’s health and behavior through wearable devices, reminding them to take medications, alerting healthcare teams to any risks, and creating personalized care plans that include emotional and social support. The goal is to see if this technology can help improve quality of life, reduce accidents like falls or emergency hospital visits, make medication management easier, and lessen the stress on family caregivers.

The study is open to adults aged 60 and older who are recognized by healthcare providers as being in the end-of-life stage, and who can use the technology either on their own or with help. Participants will be randomly divided into two groups: one receiving usual care and the other using the AI system. The study will last about six months, during which researchers will track how the AI affects the participants’ well-being and satisfaction with care. This trial aims to find out if using AI can provide better, more personalized support for seniors and their families during this important time.

Gender

ALL

Eligibility criteria

• Inclusion Criteria:
• • Age ≥ 60 years
• • Clinical diagnosis of being in the end-of-life stage, based on criteria such as the Karnofsky Performance Scale or Palliative Performance Scale
• • Informed consent obtained from the participant or legal representative
• • Ability to use technology independently or with support provided by the research team
• • Access to necessary equipment for the intervention (e.g., wearable sensors, smartphone/tablet)
• Exclusion Criteria:
• • Presence of severe cognitive impairment preventing software use
• • Voluntary withdrawal from the study at any stage
• • Critical medical deterioration or death during the study
• • Poor adherence or insufficient engagement with the intervention software in the intervention group