Kai Feng, Han Hong, Ke Tang, Jingyuan Wang
arXiv 20 Jun 2023 · Econometrics · publishedManagement Science (2025) · 41 citations (OpenAlex)
arXiv:2306.11689 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a statistical framework of using artificial intelligence to improve human decision making. The performance of each human decision maker is benchmarked against that of machine predictions. We replace the diagnoses made by a subset of the decision makers with the recommendation from the machine learning algorithm. We apply both a heuristic frequentist approach and a Bayesian posterior loss function approach to abnormal birth detection using a nationwide dataset of doctor diagnoses from prepregnancy checkups of reproductive age couples and pregnancy outcomes. We find that our algorithm on a test dataset results in a higher overall true positive rate and a lower false positive rate than the diagnoses made by doctors only.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chan, Gentzkow and Yu (2022) Selection with variation in diagnostic skill: Evidence from radiologists | 0.737 | 3 | 2 | 100% |
| 2 | Currie and MacLeod (2017) Diagnosing expertise: Human capital, decision making, and performance among physicians | 0.737 | 3 | 2 | 100% |
| 3 | Esteva, Kuprel, Novoa, Ko, Swetter, Blau and Thrun (2017) Dermatologist-level classification of skin cancer with deep neural networks | 0.693 | 5 | 1 | 100% |
| 4 | Kermany, Goldbaum, Cai, Valentim, Liang, Baxter, McKeown, Yang, Wu,… (2018) Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning | 0.693 | 5 | 1 | 100% |
| 5 | Kleinberg, Lakkaraju, Leskovec, Ludwig and Mullainathan (2018) Human decisions and machine predictions | 0.644 | 4 | 1 | 100% |
| 6 | Berk (2017) An impact assessment of machine learning risk forecasts on parole board decisions and recidivism | 0.644 | 4 | 1 | 100% |
| 7 | Fuster, Goldsmith-Pinkham, Ramadorai and Walther (2022) Predictably unequal? The effects of machine learning on credit markets | 0.644 | 4 | 1 | 100% |
| 8 | Liang, Tsui, Ni, Valentim, Baxter, Liu, Cai, Kermany, Sun, Chen et al (2019) Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence | 0.644 | 4 | 1 | 100% |
| 9 | Peng, Liu, Lv, Liu, Zhou, Yang, Ren, Liu, Wang, Zhang et al (2021) Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic st… | 0.644 | 4 | 1 | 100% |
| 10 | Bansal, Wu, Zhou, Fok, Nushi, Kamar, Ribeiro and Weld (2021) Does the whole exceed its parts? the effect of ai explanations on complementary team performance | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 109 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Statistical Inference of Optimal Allocations 1: Regularities and their Implications | 0.737 | 3 | 2 |