Scholarlink

Background <p>The aging population and rising prevalence of chronic illnesses emphasize the importance of palliative care (PC), which focuses on enhancing patients' quality of life (QoL) while supporting their families and caregivers. PC integrates multidisciplinary interventions to alleviate the physical, psychological, social, and spiritual suffering of individuals facing serious or terminal illnesses. Concurrently, Artificial Intelligence (AI) advancements have been transforming the healthcare sector, particularly through Clinical Decision Support Systems (CDSS). Leveraged by advanced algorithms and machine learning (ML), these tools analyze large volumes of data to support diagnostics, personalized treatments, and early interventions. In PC, AI has demonstrated potential to enhance early diagnosis, identify support needs, and personalize end-of-life care. ML algorithms help predict symptoms and complications, enabling timely and effective interventions. However, challenges remain, including data privacy concerns, integration into clinical workflows, and ethical implications of AI in sensitive care contexts.</p> Methods <p>We conducted a scoping review to map and analyze AI applications on PC. Articles published until May 2024 were identified in two electronic databases. From 542 records, 57 studies met the inclusion criteria. The review explored trends, benefits, and limitations of AI applications, highlighting tools for diagnostic and prognostic support, symptom tracking, shared decision-making, and communication with patients and families.</p> Results <p>The findings highlight how digital technologies and AI are revolutionizing communication, care coordination, and symptom control in PC, unlocking remote care options. The review identified key advancements in symptom management, communication, decision support, telemedicine and education areas, while addressing barriers like ethical, legal, and accessibility concerns.</p> Conclusions <p>By compiling evidence on AI use in PC, we aimed to empower professionals, researchers, and policymakers to promote more effective, ethical, and person-centered strategies. Ultimately, we provide insights for developing new technologies and establishing protocols that support the safe, equitable, and person-centered implementation of AI in palliative care, and highlight the need to prioritize early identification of patient needs, promote integration between hospital and community care, and establish protocols.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Mapping study on AI-based technologies in palliative care – a scoping study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月28日

查看全文
  • Mariana Silva-Ferreira,
  • Sara Cruz,
  • Michael Sapateiro Luís,
  • Maria Cândida Silva,
  • Sara Monteiro-Reis,
  • Rui Henrique,
  • Carmen Jerónimo,
  • Saint-Clair Lefèvre,
  • Ambre Laplaud,
  • Matthieu Frasca,
  • Lucie Pollet,
  • Lourdes Zurbanobeaskoetxea,
  • Rosana Angles Barbastro,
  • María Isabel Hidalgo García,
  • Beatriz Jiménez Galán,
  • Fátima González Palau,
  • Diana Filipa Marques,
  • Raquel Losada Durán

摘要

Background

The aging population and rising prevalence of chronic illnesses emphasize the importance of palliative care (PC), which focuses on enhancing patients' quality of life (QoL) while supporting their families and caregivers. PC integrates multidisciplinary interventions to alleviate the physical, psychological, social, and spiritual suffering of individuals facing serious or terminal illnesses. Concurrently, Artificial Intelligence (AI) advancements have been transforming the healthcare sector, particularly through Clinical Decision Support Systems (CDSS). Leveraged by advanced algorithms and machine learning (ML), these tools analyze large volumes of data to support diagnostics, personalized treatments, and early interventions. In PC, AI has demonstrated potential to enhance early diagnosis, identify support needs, and personalize end-of-life care. ML algorithms help predict symptoms and complications, enabling timely and effective interventions. However, challenges remain, including data privacy concerns, integration into clinical workflows, and ethical implications of AI in sensitive care contexts.

Methods

We conducted a scoping review to map and analyze AI applications on PC. Articles published until May 2024 were identified in two electronic databases. From 542 records, 57 studies met the inclusion criteria. The review explored trends, benefits, and limitations of AI applications, highlighting tools for diagnostic and prognostic support, symptom tracking, shared decision-making, and communication with patients and families.

Results

The findings highlight how digital technologies and AI are revolutionizing communication, care coordination, and symptom control in PC, unlocking remote care options. The review identified key advancements in symptom management, communication, decision support, telemedicine and education areas, while addressing barriers like ethical, legal, and accessibility concerns.

Conclusions

By compiling evidence on AI use in PC, we aimed to empower professionals, researchers, and policymakers to promote more effective, ethical, and person-centered strategies. Ultimately, we provide insights for developing new technologies and establishing protocols that support the safe, equitable, and person-centered implementation of AI in palliative care, and highlight the need to prioritize early identification of patient needs, promote integration between hospital and community care, and establish protocols.