arXiv subjects

Lian Xue

Publications and source records attributed to Lian Xue.

3 recordsLinked to original sources

arXiv2025

When Medical AI Explanations Help and When They Harm

We document a fundamental paradox in AI transparency: explanations improve decisions when algorithms are correct but systematically worsen them when algorithms err. In an experiment with 257 medical students making 3,855 diagnostic decisions, we find explanations increase accuracy by 6.3 percentage points when AI is correct (73% of cases) but decrease it by 4.9 points when incorrect (27% of cases). This asymmetry arises because modern AI systems generate equally persuasive explanations regardless of recommendation quality-physicians cannot distinguish helpful from misleading guidance. We show physicians treat explained AI as 15.2 percentage points more accurate than reality, with over-reliance persisting even for erroneous recommendations. Competent physicians with appropriate uncertainty suffer most from the AI transparency paradox (-12.4pp when AI errs), while overconfident novices benefit most (+9.9pp net). Welfare analysis reveals that selective transparency generates \$2.59 billion in annual healthcare value, 43% more than the \$1.82 billion from mandated universal transparency.

econ.GN↗

arXiv2024

Overcoming Medical Overuse with AI Assistance: An Experimental Investigation

This study evaluates the effectiveness of Artificial Intelligence (AI) in mitigating medical overtreatment, a significant issue characterized by unnecessary interventions that inflate healthcare costs and pose risks to patients. We conducted a lab-in-the-field experiment at a medical school, utilizing a novel medical prescription task, manipulating monetary incentives and the availability of AI assistance among medical students using a three-by-two factorial design. We tested three incentive schemes: Flat (constant pay regardless of treatment quantity), Progressive (pay increases with the number of treatments), and Regressive (penalties for overtreatment) to assess their influence on the adoption and effectiveness of AI assistance. Our findings demonstrate that AI significantly reduced overtreatment rates by up to 62% in the Regressive incentive conditions where (prospective) physician and patient interests were most aligned. Diagnostic accuracy improved by 17% to 37%, depending on the incentive scheme. Adoption of AI advice was high, with approximately half of the participants modifying their decisions based on AI input across all settings. For policy implications, we quantified the monetary (57%) and non-monetary (43%) incentives of overtreatment and highlighted AI's potential to mitigate non-monetary incentives and enhance social welfare. Our results provide valuable insights for healthcare administrators considering AI integration into healthcare systems.

econ.GN↗

arXiv2018

Tidal response of groundwater in a leaky aquifer: application to Oklahoma

Quantitative interpretation of the tidal response of water levels measured in wells has long been made either with a model for perfectly confined aquifers or with a model for purely unconfined aquifers. However, many aquifers may be neither totally confined nor purely unconfined at the frequencies of tidal loading but behave somewhere between the two end members. Here we present a more general model for the tidal response of groundwater in aquifers with both horizontal and vertical flow. The model has three independent parameters: the transmissivity and storativity of the aquifer and the specific leakage of the leaking aquitard. If transmissivity and storativity are known independently, this model may be used to estimate aquitard leakage from the phase shift and amplitude ratio of water level in wells obtained from tidal analysis. We apply the model to interpret the tidal response of water level in a USGS deep monitoring well installed in the Arbuckle aquifer in Oklahoma, into which massive amount of wastewater co-produced from hydrocarbon exploration has been injected. The analysis shows that the Arbuckle aquifer is leaking significantly at this site. We suggest that the present method may be effectively and economically applied to monitor leakage in groundwater systems, which bears on the safety of water resources, the security of underground waste repositories, and the outflow of wastewater during deep injection and hydrocarbon extraction.

physics.geo-ph↗