โก Quick Summary
This scoping study explores the integration of AI-based technologies in palliative care, highlighting their potential to enhance quality of life for patients with serious illnesses. The findings reveal significant advancements in areas such as symptom management and communication, while also addressing challenges like data privacy and ethical implications.
๐ Key Details
- ๐ Dataset: 542 records reviewed, 57 studies included
- ๐งฉ Focus: AI applications in palliative care
- โ๏ธ Technologies: Clinical Decision Support Systems (CDSS), Machine Learning (ML)
- ๐ Key Findings: Enhanced early diagnosis, symptom prediction, and personalized care
๐ Key Takeaways
- ๐ AI technologies are transforming palliative care by improving communication and care coordination.
- ๐ก Machine learning algorithms can predict symptoms and complications, enabling timely interventions.
- ๐ Telemedicine options are being unlocked, providing remote care solutions.
- โ๏ธ Ethical and legal concerns regarding AI integration in sensitive care contexts must be addressed.
- ๐ Integration between hospital and community care is crucial for effective implementation.
- ๐ Evidence compilation aims to empower professionals and policymakers in palliative care.
- ๐ ๏ธ Protocols need to be established to ensure safe and equitable AI use in palliative settings.
๐ Background
The growing aging population and increasing prevalence of chronic illnesses underscore the critical need for effective palliative care (PC). This specialized care focuses on enhancing the quality of life for patients facing serious or terminal illnesses, while also providing support to their families and caregivers. As the healthcare landscape evolves, the integration of AI technologies presents new opportunities to improve patient outcomes in this sensitive field.
๐๏ธ Study
The study conducted a comprehensive scoping review to map and analyze the applications of AI in palliative care. Researchers sifted through 542 records from two electronic databases, ultimately including 57 studies that met the inclusion criteria. The review aimed to explore trends, benefits, and limitations of AI applications, focusing on tools for diagnostic support, symptom tracking, and communication with patients and families.
๐ Results
The findings reveal that digital technologies and AI are revolutionizing communication, care coordination, and symptom control in palliative care. Key advancements were identified in areas such as symptom management, decision support, and education. However, the study also highlighted barriers, including ethical, legal, and accessibility concerns that must be navigated to fully realize the potential of AI in this field.
๐ Impact and Implications
The implications of this study are profound. By compiling evidence on AI use in palliative care, the research aims to empower healthcare professionals, researchers, and policymakers to develop more effective, ethical, and person-centered strategies. The integration of AI technologies can lead to improved early identification of patient needs and better coordination between hospital and community care, ultimately enhancing the overall quality of life for patients in palliative settings.
๐ฎ Conclusion
This scoping study highlights the transformative potential of AI in palliative care. By leveraging advanced technologies, healthcare providers can offer more personalized and effective care, addressing the unique needs of patients and their families. As we move forward, it is essential to prioritize ethical considerations and establish protocols that support the safe and equitable implementation of AI in palliative care.
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Mapping study on AI-based technologies in palliative care – a scoping study.
Abstract
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.
Author: [‘Silva-Ferreira M’, ‘Cruz S’, ‘Luรญs MS’, ‘Silva MC’, ‘Monteiro-Reis S’, ‘Henrique R’, ‘Jerรณnimo C’, ‘Lefรจvre SC’, ‘Laplaud A’, ‘Frasca M’, ‘Pollet L’, ‘Zurbanobeaskoetxea L’, ‘Barbastro RA’, ‘Garcรญa MIH’, ‘Galรกn BJ’, ‘Palau FG’, ‘Marques DF’, ‘Durรกn RL’]
Journal: BMC Palliat Care
Citation: Silva-Ferreira M, et al. Mapping study on AI-based technologies in palliative care – a scoping study. Mapping study on AI-based technologies in palliative care – a scoping study. 2025; 24:274. doi: 10.1186/s12904-025-01909-w