VLDB 2026 Research / reviewers in the wild / expert
Chris Yue Fu
dblp:430/8620
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0001-5828-5932ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Health and well-being technologies · 50% Human-robot interaction · 50% | |
| Artificial intelligence
1 paper |
Language models and text generation · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
long-term interaction |
1.0 | 1 | 2026 | More than Decision Support: Exploring Patients' Longitudinal Usage of Large Language Models in Real-World Healthcare-Seeking Journeys · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
thematic analysis · 2.0diary study · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | More than Decision Support: Exploring Patients' Longitudinal Usage of Large Language Models in Real-World Healthcare-Seeking JourneysabstractLarge language models (LLMs) have been increasingly adopted to support patients' healthcare-seeking in recent years. While prior patient-centered studies have examined the capabilities and experience of LLM-based tools in specific health-related tasks such as information-seeking, diagnosis, or decision-supporting, the inherently longitudinal nature of healthcare in real-world practice has been underexplored. This paper presents a four-week diary study with 25 patients to examine LLMs' roles across healthcare-seeking trajectories. Our analysis reveals that patients integrate LLMs not just as simple decision-support tools, but as dynamic companions that scaffold their journey across behavioral, informational, emotional, and cognitive levels. Meanwhile, patients actively assign diverse socio-technical meanings to LLMs, altering the traditional dynamics of agency, trust, and power in patient-provider relationships. Drawing from these findings, we conceptualize future LLMs as a longitudinal boundary companion that continuously mediates between patients and clinicians throughout longitudinal healthcare-seeking trajectories. Yancheng Cao, Yishu Ji, Chris Yue Fu, Sahiti Dharmavaram, Meghan Turchioe, Natalie C. Benda, Lena Mamykina, Yuling Sun, Xuhai Xu |
CHI | 3 |