Cancer survivors face unique emotional challenges that impact their quality of life. Mobile diary entries-short text entries recording through their phone about their emotional experiences-provide a promising method for tracking these experiences in real time. Although emotion analysis tools show potential for recognizing emotions from text, current methods lack the contextual understanding necessary to accurately interpret the brief, personal narratives in mobile diaries. We propose CALLM, a context-aware emotion analysis framework that leverages Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), to analyze mobile diary entries from cancer survivors to predict their emotional states. The framework enhances prediction accuracy beyond existing methods by (1) integrating retrieved peer experiences as contextual examples and (2) incorporating individuals' temporal emotional trajectories from their mobile diary entries. We collected a large-scale dataset (N=407) of cancer survivors' mobile ecological momentary assessments (EMAs), which assessed positive and negative affect, desire to regulate emotions, social interaction quality, and availability for interventions, alongside daily mobile diary entries in an open response format regarding what was driving their current emotional experience. Results demonstrate strong performance of CALLM, with balanced accuracies reaching 72.96% for positive and 73.29% for negative affect, and 73.72% for predicting individual's desire to regulate emotions. Post-hoc analysis reveals that leveraging model confidence, encouraging longer diary entries, and incorporating personal ground truth, further enhance predictive outcomes. Our findings support the feasibility of deploying LLM-powered emotion analysis in chronic health populations and suggest promising directions for personalized interventions for cancer survivors.

Content analysis breaks down complex and unstructured texts into theory-informed numerical categories. Particularly, in social science, this process usually relies on multiple rounds of manual annotation, domain expert discussion, and rule-based refinement. In this paper, we introduce SCALE, a novel multi-agent framework that effectively $\underline{\textbf{S}}$imulates $\underline{\textbf{C}}$ontent $\underline{\textbf{A}}$nalysis via $\underline{\textbf{L}}$arge language model (LLM) ag$\underline{\textbf{E}}$nts. SCALE imitates key phases of content analysis, including text coding, collaborative discussion, and dynamic codebook evolution, capturing the reflective depth and adaptive discussions of human researchers. Furthermore, by integrating diverse modes of human intervention, SCALE is augmented with expert input to further enhance its performance. Extensive evaluations on real-world datasets demonstrate that SCALE achieves human-approximated performance across various complex content analysis tasks, offering an innovative potential for future social science research.

The use of Artificial Intelligence (AI) in healthcare, including in caring for cancer survivors, has gained significant interest. However, gaps remain in our understanding of how such AI systems can provide care, especially for ethnic and racial minority groups who continue to face care disparities. Through interviews with six cancer survivors, we identify critical gaps in current healthcare systems such as a lack of personalized care and insufficient cultural and linguistic accommodation. AI, when applied to care, was seen as a way to address these issues by enabling real-time, culturally aligned, and linguistically appropriate interactions. We also uncovered concerns about the implications of AI-driven personalization, such as data privacy, loss of human touch in caregiving, and the risk of echo chambers that limit exposure to diverse information. We conclude by discussing the trade-offs between AI-enhanced personalization and the need for structural changes in healthcare that go beyond technological solutions, leading us to argue that we should begin by asking, ``Why personalization?''

Ovarian tissue frozen 11 years ago has been transplanted back into the womb of a 26-year-old cancer survivor using a robot. The woman was 15 when she decided to have her eggs removed so she could have a chance of starting a family despite undergoing fertility-crushing chemotherapy to cure her recurrent leukemia. Her tissue was removed by Dr Kutluk Oktay, the pioneering NYU Winthrop reproductive specialist and ovarian biologist who invented the precise freezing procedure in 1999. Now newly-married, the patient - who wishes to remain unidentified until she conceives - has returned to Dr Oktay's clinic to have her tissue thawed and re-implanted so she can start a family. 'This procedure is literally life-changing.

If you're trying to get in shape and you want a tiny, wrist-bound computer to help you do it, you have more options than ever before. Fitness trackers come in all shapes, colors, and price tags, with newfangled sensors and features to stand out to customers. But for doctors and scientists studying how exercise can help people deal with disease, the landscape is much simpler. Like most fitness trackers, Fitbit's devices are far from perfect. They can count steps pretty well and give a good idea of activity levels day to day.

Once upon a time, there was a man who worked for a technology company. He found out he had an advanced cancer. He tried the "one-size-fits-all" chemo/radiation treatments, but he didn't get any better. So he went to the healthcare group at his company, asked to work there, and became his own advocate. And on his journey from cancer patient to cancer survivor, he discovered something amazing: "AI is going to solve the problems that humans can't," said Bryce Olson, cancer survivor and global marketing director of the Health and Life Sciences Group at Intel.