arXiv subjects

T. Y. Alvin Liu

Publications and source records attributed to T. Y. Alvin Liu.

3 recordsLinked to original sources

arXiv2026

CHI-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?

End-to-end automation of realistic healthcare operations stresses three capabilities underrepresented in current benchmarks: policy density, decisions must be grounded in a large library of medical, insurance, and operational rules; Multi-role composition: a single task requires the agent to play multiple roles with handoffs; and multilateral interaction: intermediate workflow steps are multi-turn dialogs, such as peer-to-peer review and patient outreach. We introduce $χ$-Bench, a benchmark of long-horizon healthcare workflows across three domains: provider prior authorization, payer utilization management, and care management. Each task hands the agent a clinical case in a high-fidelity simulator of 20 healthcare apps exposed via 87 MCP tools, which it must drive to a terminal status through tool calls and writing the role's artifacts, guided by a 1,290+ document managed-care operations handbook skill. Across 30 agent harness/models configurations, the best agent resolves only 28.0% of tasks, no agent clears 20% on strict pass^3, and executing all tasks in a single session slumps the performance to 3.8%. These results raise the hypothesis that similar gaps are likely to surface in other policy-dense, role-composed, irreversible enterprise domains.

cs.CL↗

arXiv2025

$\textit{In vivo}$ fundus imaging and computational refocusing with a diffuser-based fundus camera

Significance: Access to diagnostic eye care could be expanded with high-throughput and easy-to-use tools. Phase mask-based imaging may improve the fundus camera by enabling computational refocusing with no moving parts. While phase mask-based imaging has been demonstrated in a model eye, this approach has not been shown $\textit{in vivo}$. Aim: A computational fundus camera was designed, constructed, and evaluated with the goal of determining the feasibility and performance of phase mask-based computational imaging of the $\textit{in vivo}$ fundus. Approach: A holographic diffuser was introduced in a modified commercial fundus camera at a plane conjugate to the ocular pupil, resulting in a linear and shift-invariant point spread function that varies with refractive error. The image could be digitally refocused across a range of $\geq\pm$ 10 diopters of defocus error. The device was tested for ocular safety, and a human imaging pilot study was performed. Results: The device captured and digitally refocused color human fundus images. The field of view was $\geq$35 degrees and resolution was 7.7-9.6 line pairs per mm. Conclusions: We present the first $\textit{in vivo}$ diffuser-based fundus images, demonstrating the feasibility of computational imaging for ocular diagnostics.

physics.optics↗

arXiv2021

An Interpretable Algorithm for Uveal Melanoma Subtyping from Whole Slide Cytology Images

Algorithmic decision support is rapidly becoming a staple of personalized medicine, especially for high-stakes recommendations in which access to certain information can drastically alter the course of treatment, and thus, patient outcome; a prominent example is radiomics for cancer subtyping. Because in these scenarios the stakes are high, it is desirable for decision systems to not only provide recommendations but supply transparent reasoning in support thereof. For learning-based systems, this can be achieved through an interpretable design of the inference pipeline. Herein we describe an automated yet interpretable system for uveal melanoma subtyping with digital cytology images from fine needle aspiration biopsies. Our method embeds every automatically segmented cell of a candidate cytology image as a point in a 2D manifold defined by many representative slides, which enables reasoning about the cell-level composition of the tissue sample, paving the way for interpretable subtyping of the biopsy. Finally, a rule-based slide-level classification algorithm is trained on the partitions of the circularly distorted 2D manifold. This process results in a simple rule set that is evaluated automatically but highly transparent for human verification. On our in house cytology dataset of 88 uveal melanoma patients, the proposed method achieves an accuracy of 87.5% that compares favorably to all competing approaches, including deep "black box" models. The method comes with a user interface to facilitate interaction with cell-level content, which may offer additional insights for pathological assessment.

cs.CV↗