Cost-Effectiveness of AI for Risk-Stratified Breast Cancer Screening.

Importance: Previous research has shown good discrimination of short-term risk using an artificial intelligence (AI) risk prediction model (Mirai). However, no studies have been undertaken to evaluate whether this might translate into economic gains. Objective: To assess the cost-effectiveness of incorporating risk-stratified screening using a breast cancer AI model into the United Kingdom (UK) National Breast Cancer Screening Program. Design, Setting, and Participants: This study, conducted from January 1, 2023, to January 31, 2024, involved the development of a decision analytical model to estimate health-related quality of life, cancer survival rates, and costs over the lifetime of the female population eligible for screening. The analysis took a UK payer perspective, and the simulated cohort consisted of women aged 50 to 70 years at screening. Exposures: Mammography screening at 1 to 6 yearly screening intervals based on breast cancer risk and standard care (screening every 3 years). Main Outcomes and Measures: Incremental net monetary benefit based on quality-adjusted life-years (QALYs) and National Health Service (NHS) costs (given in pounds sterling; to convert to US dollars, multiply by 1.28). Results: Artificial intelligence-based risk-stratified programs were estimated to be cost-saving and increase QALYs compared with the current screening program. A screening schedule of every 6 years for lowest-risk individuals, biannually and triennially for those below and above average risk, respectively, and annually for those at highest risk was estimated to give yearly net monetary benefits within the NHS of approximately £60.4 (US $77.3) million and £85.3 (US $109.2) million, with QALY values set at £20 000 (US $25 600) and £30 000 (US $38 400), respectively. Even in scenarios where decision-makers hesitate to allocate additional NHS resources toward screening, implementing the proposed strategies at a QALY value of £1 (US $1.28) was estimated to generate a yearly monetary benefit of approximately £10.6 (US $13.6) million. Conclusions and Relevance: In this decision analytical model study of integrating risk-stratified screening with a breast cancer AI model into the UK National Breast Cancer Screening Program, risk-stratified screening was likely to be cost-effective, yielding added health benefits at reduced costs. These results are particularly relevant for health care settings where resources are under pressure. New studies to prospectively evaluate AI-guided screening appear warranted.

Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks.

Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the financial domain with its advanced data processing capabilities. This paper introduces Factor-GAN, an innovative framework that utilizes Generative Adversarial Networks (GAN) technology for factor investing. Leveraging a comprehensive factor database comprising 70 firm characteristics, Factor-GAN integrates deep learning techniques with the multi-factor pricing model, thereby elevating the precision and stability of investment strategies. To explain the economic mechanisms underlying deep learning, we conduct a subsample analysis of the Chinese stock market. The findings reveal that the deep learning-based pricing model significantly enhances return prediction accuracy and factor investment performance in comparison to linear models. Particularly noteworthy is the superior performance of the long-short portfolio under Factor-GAN, demonstrating an annualized return of 23.52% with a Sharpe ratio of 1.29. During the transition from state-owned enterprises (SOEs) to non-SOEs, our study discerns shifts in factor importance, with liquidity and volatility gaining significance while fundamental indicators diminish. Additionally, A-share listed companies display a heightened emphasis on momentum and growth indicators relative to their dual-listed counterparts. This research holds profound implications for the expansion of explainable artificial intelligence research and the exploration of financial technology applications.

How Should Medicare Pay for Artificial Intelligence?

This Viewpoint examines artificial intelligence­enabled clinical services, existing payment structures, and the economics of artificial intelligence pricing.

Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI).

OBJECTIVES: Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner. METHODS: Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalize outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective. RESULTS: A total of 58 participants responded to survey round 1, 42, and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported. CONCLUSIONS: CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions.

Supporting equitable and responsible highway safety improvement funding allocation strategies - Why AI prediction biases matter.

The existing methodologies for allocating highway safety improvement funding closely rely on the utilization of crash prediction models. Specifically, these models produce predictions that estimate future crash hazard levels in different geographical areas, which subsequently support the future funding allocation strategies. In recent years, there is a burgeoning interest in applying artificial intelligence (AI)-based models to perform crash prediction tasks. Despite the remarkable accuracy of these AI-based crash prediction models, they have been observed to yield biased prediction outcomes across areas of different socioeconomic statuses. These biases are primarily attributed to the inherent measurement and representation biases of AI-based prediction models. More precisely, measurement bias arises from the selection of target variables to reflect crash hazard levels, while representation bias results from the issue of imbalanced number of samples representing areas with different socioeconomic statuses within the dataset. Consequently, these biased prediction outcomes have the potential to perpetuate an unfair allocation of funding resources, contributing to worsen social inequality over time. Drawing upon a real-world case study in North Carolina, this study designs an AI-based crash prediction model that utilizes previous sociodemographic and crash-related variables to predict future severe crash rate of each area to reflect the crash hazardous level. By incorporating a fair regression framework, this study endeavors to transform the crash prediction model to become both fair and accurate, aiming to support equitable and responsible safety improvement funding allocation strategies.

Prevalence, mortality, cost, and disparities in transcatheter mitral valve repair and replacement in cancer patients: Artificial intelligence and propensity score national 5-year analysis of 7495 procedures.

INTRODUCTION: We conducted the first comprehensive evaluation of the therapeutic value and safety profile of transcatheter mitral edge-to-edge repair (TEER) and transcatheter mitral valve replacement (TMVR) in individuals concurrently afflicted with cancer. METHODS: Utilizing the National Inpatient Sample (NIS) dataset, we analyzed all adult hospitalizations between 2016 and 2020 (n = 148,755,036). The inclusion criteria for this retrospectively analyzed prospective cohort study were all adult hospitalizations (age 18 years and older). Regression and machine learning analyses in addition to model optimization were conducted using ML-PSr (Machine Learning-augmented Propensity Score adjusted multivariable regression) and BAyesian Machine learning-augmented Propensity Score (BAM-PS) multivariable regression. RESULTS: Of all adult hospitalizations, there were 5790 (0.004%) TMVRs and 1705 (0.001%) TEERs. Of the total TMVRs, 160 (2.76%) were done in active cancer. Of the total TEERs, 30 (1.76%) were done in active cancer. After the comparable rates of TEER/TMVR in active cancer in 2016, the prevalence of TEER/TMVR was significantly less in active cancer from 2017 to 2020 (2.61% versus 7.28% p < 0.001). From 2017 to 2020, active cancer significantly decreased the odds of receiving TEER or TMVR (OR 0.28, 95%CI 0.13-0.68, p = 0.008). In patients with active cancer who underwent TMVR/TEER, there were no significant differences in socio-economic disparities, mortality or total hospitalization costs. CONCLUSION: The presence of malignancy does not contribute to increased mortality, length of stay or procedural costs in TMVR or TEER. Whereas the prevalence of TMVR has increased in patients with active cancer, the utilization of TEER in the context of active cancer is declining despite a growing patient population.

A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality.

Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field of ophthalmology. Nevertheless, there is a lack of systematic studies on the health economics of AI in this field. We examine studies from the PubMed, Google Scholar, and Web of Science databases that employed quantitative analysis, retrieved up to July 2023. Most of the studies indicate that AI leads to cost savings and improved efficiency in ophthalmology. On the other hand, some studies suggest that using AI in healthcare may raise costs for patients, especially when taking into account factors such as labor costs, infrastructure, and patient adherence. Future research should cover a wider range of ophthalmic diseases beyond common eye conditions. Moreover, conducting extensive health economic research, designed to collect data relevant to its own context, is imperative.

Examining different cost ratio frameworks for decision rule machine learning algorithms in diagnostic application.

BACKGROUND: Artificial Intelligence (AI) plays a pivotal role in the diagnosis of health conditions ranging from general well-being to critical health issues. In the realm of health diagnostics, an often overlooked but critical aspect is the consideration of cost-sensitive learning, a facet that this study prioritizes over the non-invasive nature of the diagnostic process whereas the other standard metrics such as accuracy and sensitivity reflect weakness in error profile. OBJECTIVE: This research aims to investigate the total cost of misclassification (Total Cost) by decision rule Machine Learning (ML) algorithms implemented in Java platforms such as DecisionTable, JRip, OneR, and PART. An augmented dataset with conjunctiva images along candidates' demographic and anthropometric features under supervised learning is considered with a specific emphasis on cost-sensitive classification. METHODS: The opted decision rule classifiers use the text features, additionally the image feature 'a* value of CIELAB color space' extracted from the conjunctiva digital images as input attributes. The pre-processing consists of amalgamating text and image features on a uniform scale, normalizing. Then the 10-fold cross-validation enables the classification of samples into two categories: the presence or absence of the anemia. This study utilizes the Cost Ratio (ρ) extracted from the cost matrix to meticulously monitor the Total Cost in four different cost ratio methodologies namely Uniform (U), Uniform Inverted (UI), Non-Uniform (NU), and Non-Uniform Inverted (NUI). RESULTS: It has been established that the PART classifier stands out as the top performer in this binary classification task, yielding the lowest mean total cost of 629.9 compared to other selected classifiers. Moreover, it demonstrates a comparatively lower standard deviation 335.9, and lower total cost across all four different cost ratio methodologies. The ranking of algorithm performance goes as follows: PART, JRIP, DecisionTable, and OneR. CONCLUSION: The significance of adopting a cost-sensitive learning approach is emphasized showing the PART classifier's consistent performance within the proposed framework for learning the anemia dataset. This emphasis on cost-sensitive learning not only enhances the recommendations in diagnosis but also holds the potential for substantial cost savings and makes it a noteworthy focal point in the advancement of AI-driven health care.