In the fields of healthcare and medicine research, the fusion of Artificial Intelligence (AI) and Data Science (DS) has brought unprecedented innovation to this traditional domain. This integration has demonstrated its potential in multiple aspects, including but not limited to medical imaging analysis, diagnostic decision support, drug discovery, and personalized healthcare. Utilizing AI and DS, the healthcare field has achieved significant advancements in enhancing diagnostic accuracy and treatment efficiency. Despite the promising outlook, "AI and Data Science for Healthcare" still faces several key gaps and challenges in realizing its full potential: 

Traditionally, the development of AI has focused on optimizing model design to accommodate fixed datasets, often overlooking inherent data flaws such as missing values, incorrect labels, and outliers. This situation raises a critical issue: whether improvements in various model performances genuinely reflect the model's actual potential or merely result from overfitting the datasets. Medical professionals and researchers typically emphasize the importance of large-scale, high-quality datasets for generating reliable and broadly applicable medical insights, highlighting the necessity of implementing Data-Centric AI (DCAI). 

Although advancements in AI and DS have provided people with more access to healthcare information, these technologies have not sufficiently encouraged active people participation. Existing healthcare information systems lack interactivity and user-friendliness, restricting people's ability to proactively track, manage, and share their healthcare data. Therefore, developing interactive and user-friendly people-centric AI technologies and providing corresponding data education and support are crucial for realizing People-Centric Healthcare (PCHC). 

A lack of interoperability among healthcare IT systems and a unified standard for data exchange limits the effective use of Data-Centric AI in People-Centric Healthcare. The diversity of data formats and standards impedes data sharing and analysis, restricting the ability to provide comprehensive and coordinated care. To promote the effective merger of Data-Centric AI and People-Centric Healthcare, there is an urgent need to develop and adopt unified data standards. Effectively integrating data from various sources, including clinical data, people-provided information, and real-time healthcare monitoring data, to support personalized medicine remains a significant challenge. 

AI models have shown tremendous potential in improving the accuracy of medical diagnoses and treatment decisions. However, these models are often seen as "black boxes" with insufficient explainability, which poses a barrier to building trust in AI-driven decisions among people and healthcare providers. Enhancing the explainability of models and ensuring that both people and medical professionals can understand the recommendations made by AI is crucial for advancing people-centric AI applications. 

The overarching goal of this workshop is to grow the AI/DS for Healthcare/Medicine communities and bridge these gaps. We aim to discuss scientific advances and thoughtful views generated from collaboration between healthcare professionals and experts from the fields of artificial intelligence and data science, further promoting the integration and development of DCAI and PCHC. We especially focus on efforts that have the potential to translate the value of healthcare data for the benefit of people's healthcare and the population's well-being. We believe the above topics have a broad audience across the entire SIGKDD research community.