Machine Learning in Healthcare: Advancements and Challenges

EasyChair Preprint 12572

Abstract

Machine learning techniques have revolutionized various industries, and healthcare is no exception. This abstract provides an overview of the advancements and challenges associated with the application of machine learning in healthcare.

Advancements in machine learning have enabled healthcare professionals to extract valuable insights from vast amounts of healthcare data, leading to improved diagnostic accuracy, personalized treatment plans, and efficient healthcare delivery. Machine learning algorithms can analyze complex patterns in medical images, electronic health records, genomic data, and wearable devices, assisting in early disease detection, risk prediction, and treatment optimization. Moreover, machine learning models have the potential to enhance clinical decision-making by integrating multiple data sources and providing real-time recommendations.

However, the implementation of machine learning in healthcare also presents several challenges. One significant challenge is the requirement of high-quality and diverse data for training accurate and reliable models. Access to large, well-annotated datasets can be limited due to privacy concerns, data silos, and interoperability issues. Additionally, ensuring the transparency, interpretability, and explainability of machine learning models is crucial in the healthcare domain. Ethical considerations, such as bias mitigation and fairness, need to be addressed to prevent algorithmic biases that could disproportionately impact vulnerable populations.

Keyphrases: Health, Insurance, Portability

@booklet{EasyChair:12572,
  author    = {Favour Olaoye and Kaledio Potter and Lucas Doris},
  title     = {Machine Learning in Healthcare: Advancements and Challenges},
  howpublished = {EasyChair Preprint 12572},
  year      = {EasyChair, 2024}}