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Related Concept Videos

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Health promotion allows a person to control the determinants of health, resulting in an improved health status. It enhances the quality of life and reduces premature deaths. Health promotion and illness prevention programs help people make beneficial choices to reduce the risk of disease and disabilities. There are three health promotion and illness prevention levels: primary, secondary, and tertiary prevention.
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Related Experiment Video

Updated: Jan 16, 2026

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
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Artificial Intelligence for Serious Illness Communication: Proactive Approaches to Mitigating Harm.

Elise C Tarbi1, Brigitte N Durieux2, Anne Kwok3

  • 1Dana-Farber Cancer Institute, Boston, USA, University of Vermont, Burlington, USA.

The Journal of Medicine and Philosophy
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Artificial Intelligence (AI) can enhance serious illness communication in palliative care. However, researchers must carefully address potential biases in AI models and data to ensure equitable and high-quality care for all patients.

Keywords:
artificial intelligencebioethicscommunicationpalliative careserious illness

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Area of Science:

  • Palliative Care
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Serious illness communication is central to palliative care, aiming to align medical interventions with patient values and enhance patient/family experiences.
  • Artificial Intelligence (AI) offers potential for analyzing and improving communication through methods like speech pattern capture and feedback delivery.
  • Existing disparities in palliative care and limitations in communication datasets necessitate a cautious approach to AI implementation.

Purpose of the Study:

  • To examine the assumptions embedded within AI models used in palliative care communication.
  • To identify strategies for mitigating potential harm stemming from AI in serious illness communication.
  • To promote equitable and high-quality palliative care through thoughtful AI innovation.

Main Methods:

  • Reviewing assumptions in AI model development, including data collection, definitions, measurements, and outcome assessments.
  • Considering potential biases in natural communication datasets.
  • Proposing mitigation strategies across the AI model lifecycle.

Main Results:

  • AI presents opportunities for measuring and enhancing serious illness communication.
  • Potential biases in data, definitions, measurements, and outcomes must be critically evaluated.
  • Mitigation strategies are crucial for equitable AI application in palliative care.

Conclusions:

  • Careful consideration of assumptions and potential biases is essential for responsible AI development in palliative care.
  • Inclusive data collection and incorporating patient-family feedback are key to mitigating harm.
  • Transparent and thoughtful AI innovation can lead to more equitable and higher-quality serious illness care.