Sustainable Data Science in Healthcare: Harnessing AI for Better Patient Outcomes

EasyChair Preprint 12216

Abstract

In recent years, the intersection of data science and healthcare has paved the way for transformative advancements in patient care, diagnosis, and treatment. Leveraging artificial intelligence (AI) and machine learning (ML) algorithms, healthcare providers are increasingly able to harness the vast amounts of data generated within healthcare systems to improve patient outcomes. However, as the reliance on AI in healthcare grows, it is imperative to ensure that these technologies are deployed sustainably, ethically, and with a focus on long-term benefits. This paper explores the concept of sustainable data science in healthcare, emphasizing the importance of responsible AI implementation to address current challenges and opportunities. We examine the key pillars of sustainable data science, including data governance, algorithm transparency and interpretability, privacy protection, and equity in healthcare access. By integrating these principles into the development and deployment of AI-driven healthcare solutions, stakeholders can mitigate potential risks while maximizing the benefits for patients and healthcare providers alike. Furthermore, this paper highlights case studies and examples of successful AI applications in healthcare, illustrating how sustainable data science practices can lead to improved patient outcomes, enhanced clinical decision-making, and operational efficiencies. From predictive analytics for early disease detection to personalized treatment recommendations, AI-powered tools have the potential to revolutionize healthcare delivery.

Keyphrases: Artificial Intelligence (AI), Healthcare, Sustainable data science

@booklet{EasyChair:12216,
  author    = {Jane Elsa and Feriha Hamid},
  title     = {Sustainable Data Science in Healthcare: Harnessing AI for Better Patient Outcomes},
  howpublished = {EasyChair Preprint 12216},
  year      = {EasyChair, 2024}}