About

Mehmet Eren Ahsen is an Assistant Professor in the Biomedical and Translational Sciences department at the Carle Illinois College of Medicine, University of Illinois Urbana-Champaign. His research focuses on integrating business analytics, business intelligence, and innovative technologies to improve medical screening processes, such as mammogram screenings. He has been recognized for blending business and technology to advance health innovation, including winning a Young Researcher Award. He teaches courses related to business analytics, business intelligence, and information systems, contributing to the educational mission of the college. His work involves clinical research and innovation, aiming to reengineer medicine through the application of engineering principles and data-driven approaches.

Research topics

• Artificial Intelligence
• Computer Science
• Political Science
• Medicine
• Psychology
• Data science
• Biology
• Internal medicine
• Computational biology
• Applied psychology
• Medical physics
• Radiology
• Knowledge management
• Genetics

Selected publications

• Modeling workflow, operational, and financial implications of AI-enabled same-day diagnostic follow-up for screening mammograms

  2026-04-02

  articleOpen access

  While much of the evaluation of artificial intelligence (AI) in healthcare has focused on technical performance metrics such as accuracy or area under the curve, real-world adoption critically depends on how AI reshapes clinical workflows, operations, and revenue streams. Simulation models provide a means to anticipate these impacts before implementation, allowing stakeholders to weigh benefits against potential harm. In this study, we used discrete-event simulation to evaluate an AI-assisted workflow for same-day diagnostic breast imaging following abnormal screening mammograms. The revised workflow captured an additional of 1.1% mammography screening patients who might otherwise be lost to follow-up. It also eliminated the need for a second visit for diagnostic workup for 11% of mammography screening patients. It also increased daily work relative value units by 4.8%, translating to an estimated $15,979 in additional annual gain, while extending clinic operating hours by 2.9%, amounting to 109.5 hours annually. These findings highlight how simulation modeling can inform the operational and financial implications of AI adoption in imaging workflows in clinical practice.

  PublisherOA PDFDOI

• Leveraging Generative AI for Interpretable Clinical Decision Making Through Causal Graphs

  2025-12-01

  articleOpen access1st authorCorresponding

  Clinical AI systems' lack of interpretability limits their adoption in evidence-based medicine. To address this challenge, we propose a computational framework that harnesses generative AI's medical knowledge to create interpretable structural causal models (SCMs) for clinical decision support, quality improvement evaluation, and population health management. We evaluated our approach through a case study using data from the Midwest Healthcare Conference Causal Diagram Challenge, where we compared transformer-based large language models against human performance on a complex causal reasoning task: estimating COVID- 19 treatment effects through target trial emulation. Both groups designed SCMs to evaluate glucocorticoid treatment effects on 28-day mortality using real-world data from more than 2,000 hospitalized patients, benchmarked against published RECOVERY randomized controlled trial results. The best performing SCMs achieved bootstrap coverage rates exceeding 90% for two of three severity strata. Both human and AI models demonstrated equivalent clinical plausibility (n=3 expert reviewers) and similar statistical performance, though both struggled with critical disease severity. Ablation experiments comparing SCM-based approaches against traditional potential outcomes methods revealed SCMs achieved 76-98% coverage versus 1-37% for traditional methods. These results suggest that structural causal models can effectively bridge the interpretability gap in clinical AI by providing essential scaffolding for reliable causal inference and enabling meaningful human-AI collaboration while preserving methodological rigor essential for evidence-based medicine.

  PublisherDOI

• Individual Versus Institutional Philanthropy: Crowdfunding During Crises

  Production and Operations Management · 2025-10-16

  articleSenior author

  Crises are accompanied by resource shortages, operational challenges, and shifts in demand for resources. During crises, nonprofit organizations have historically played a crucial role in mitigating these challenges, with crowdfunding platforms emerging as a new solution for resource mobilization. In this study, we examine how individual and institutional donors respond to the sudden shifts in demand that accompany a global crisis. We analyze rich, granular data from multiple sources, including donation data from a leading education crowdfunding platform, before and after the onset of the COVID-19 pandemic, and find notable contrasts between individual and institutional responses. While there was a significant increase in institutional donations during the pandemic, individual donations remained relatively stable overall. Adopting a motivation-opportunity-ability (MOA) framework, we center donor heterogeneity in our approach, deriving nuanced insights. Donations from individuals in economically disadvantaged communities, which were the worst impacted by the crisis, decreased; however, these communities prioritized immediate and time-sensitive needs. Small and local institutions played a pivotal role in supporting high-need schools during the crisis. These findings make meaningful contributions to the operations literature on crowdfunding and have practical implications for platform designers. By distinguishing between individual and institutional donors and identifying the factors that drive their behavior, our study offers important implications for platform designers to improve crisis-time resource mobilization through more targeted engagement strategies.

  PublisherDOI

• Leveraging Social Media for COVID-19 Response: Insights from a Data Competition

  Foundations and Trends® in Technology Information and Operations Management · 2025-03-12

  articleOpen access1st authorCorresponding

  The COVID-19 pandemic accelerated the adoption of digital platforms across various sectors, notably in education and healthcare, with remote learning and social media emerging as pivotal tools for communication and crisis management. Social networks played a crucial role in disseminating critical information, combating misinformation, and fostering community engagement. Recent research underscores the significance of social media in shaping public behavior towards adopting protective measures against COVID-19, yet quantifying its precise impact remains challenging due to the complexity of social relationships and diverse information sources. Multimodal data generated by social media platforms presents opportunities for more insightful Machine Learning (ML) models, but also poses technical challenges in data integration and interpretation. Leveraging crowdsourcing, we organized a data science competition aimed at forecasting COVID-19 positivity rates and identifying factors influencing its spread using infection and social media data. The competition facilitated collaborative problemsolving and provided actionable insights for public health communication and policy-making. This study outlines the competition structure, methodologies employed by participants, key findings, and implications for future pandemics and public health crises.

  PublisherDOI

• Harnessing Unsupervised Ensemble Learning for Biomedical Applications: A Review of Methods and Advances

  Mathematics · 2025-01-27 · 4 citations

  reviewOpen access1st authorCorresponding

  Advancements in data availability and computational techniques, including machine learning, have transformed the field of bioinformatics, enabling the robust analysis of complex, high-dimensional, and heterogeneous biomedical data. This paper explores how diverse bioinformatics tasks, including differential expression analysis, network inference, and somatic mutation calling, can be reframed as binary classification tasks, thereby providing a unifying framework for their analysis. Traditional single-method approaches often fail to generalize across datasets due to differences in data distributions, noise levels, and underlying biological contexts. Ensemble learning, particularly unsupervised ensemble approaches, emerges as a compelling solution by integrating predictions from multiple algorithms to leverage their strengths and mitigate weaknesses. This review focuses on the principles and recent advancements in ensemble learning, with a particular emphasis on unsupervised ensemble methods. These approaches demonstrate their ability to address critical challenges in bioinformatics, such as the lack of labeled data and the integration of predictions from algorithms operating on different scales. Overall, this paper highlights the transformative potential of ensemble learning in advancing predictive accuracy, robustness, and interpretability across diverse bioinformatics applications.

  PublisherOA PDFDOI

• Expert Detection on Crowdsourcing Forums via Sheaf Laplacian

  2025-10-09

  articleSenior author

  Expert detection within crowdsourcing forums is crucial for enhancing content accuracy and the decision-making process, as well as identifying knowledgeable individuals on a certain topic. While current expert detection methods identify knowledgeable users effectively, these methods depend on various user behavioral statistics, which may not be available for some crowdsourcing forums, and they only recognize expert users at the forum level on a general topic, such as data science, losing specific expertise on subtopics, such as clustering. In this paper, we propose a novel method that identifies experts for each subtopic using only the user's interactions and ratings, without relying on additional user behavioral statistics. We define responding/commenting to the same post/questions as the user interaction and create graphs and hypergraphs via these interactions. Then, we define a sheaf data structure on these networks to keep each user's tag-based knowledge and connections. To model information diffusion within the network considering subtopics, we develop a novel network diffusion model via sheaf Laplacian that captures subtopic-based knowledge diffusion over the network. Furthermore, we define a new centrality method, called sheaf Laplacian centrality, to measure a given user's expertise in each subtopic. Through extensive experiments conducted on four Stack Exchange networks, we show that our models outperform baseline models in detecting subtopic-based experts.

  PublisherDOI

• Investigating the role of AI explanations in lay individuals’ comprehension of radiology reports: A metacognition lens

  PLoS ONE · 2025-09-10

  articleOpen accessCorresponding

  While there has been extensive research on techniques for explainable artificial intelligence (XAI) to enhance AI recommendations, the metacognitive processes in interacting with AI explanations remain underexplored. This study examines how AI explanations impact human decision-making by leveraging cognitive mechanisms that evaluate the accuracy of AI recommendations. We conducted a large-scale experiment (N = 4,302) on Amazon Mechanical Turk (AMT), where participants classified radiology reports as normal or abnormal. Participants were randomly assigned to three groups: a) no AI input (control group), b) AI prediction only, and c) AI prediction with explanation. Our results indicate that AI explanations enhanced task performance. Our results indicate that explanations are more effective when AI prediction confidence is high or users' self-confidence is low. We conclude by discussing the implications of our findings.

  PublisherOA PDFDOI

• Economics of AI and human task sharing for decision making in screening mammography

  Nature Communications · 2025-03-07 · 9 citations

  articleOpen access1st authorCorresponding

  The rising global incidence of breast cancer and the persistent shortage of specialized radiologists have heightened the demand for innovative solutions in mammography screening. Artificial intelligence (AI) has emerged as a promising tool to bridge this demand-supply gap, with potential applications ranging from full automation to integrated AI-human decision-making. This study evaluates the economic feasibility of incorporating artificial intelligence (AI) into mammography screening within healthcare settings, considering full or partial integration. To evaluate the economic viability, we employ an optimization model specifically designed to minimize mammography screening costs. This model considers three distinct approaches when interpreting mammograms: automation strategy utilizing AI exclusively, delegation strategy involving the selective allocation of tasks between radiologists and AI, and the expert-alone strategy relying solely on radiologist decisions. Our findings underscore the significance of disease prevalence in relation to the trade-off between costs associated with false positives (e.g., follow-up expenses) and false negatives (e.g., litigation costs stemming from missed diagnoses) in shaping the AI strategy for healthcare organizations. We backtest our approach using data from an AI contest in which participants aimed to match or surpass radiologists' performance in assessing screening mammograms for women. The contest data supports the optimality of the delegation strategy, potentially leading to cost savings of 17.5% to 30.1% compared to relying solely on human experts. Our research provides guidance for healthcare organizations considering AI integration in mammography screening, with broader implications for work design and human-AI hybrid solutions in various fields.

  PublisherOA PDFDOI

• Will Machines Take Over? Algorithms for Human-Machine Collaborative Decision Making in Healthcare

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Investigating the Role of AI Explanations in Lay Individuals’ Comprehension of Radiology Reports: A Metacognition Lense

  medRxiv · 2025-03-10

  preprintOpen access

  Much research has focused on advancing techniques for explainable artificial intelligence (XAI) to improve the utility of AI recommendations. However, the metacognitive processes involved in interacting with AI explanations have not been fully explored. In this study, we examine the effects of AI explanations on human decisions from the perspective of cognitive mechanisms that evaluate the correctness of AI recommendations. To accomplish this, we conducted a large-scale, between-subject experiment (N=4,302) on Amazon Mechanical Turk, during which each participant was asked to classify a radiology report as describing a normal or abnormal finding. The participants were randomly assigned into three different groups: a) without accompanying AI input (control group,) b) with AI prediction only, and c) with AI prediction and AI explanation. Our results show that AI explanations improved the overall task performance. We hypothesize that explanations help decision-makers better evaluate their intuitions about their decisions—a process known as self-monitoring—and, as such, overcome their cognitive limitations and compensate for machine prediction errors. Additionally, our results show that explanations are more effective when AI prediction confidences are high or users' self-confidence is low. We conclude this paper by discussing the theoretical and practical implications of our findings.

  PublisherOA PDFDOI

Frequent coauthors

• Gustavo Stolovitzky

  Sema4 (United States)

  60 shared

• Silviu‐Iulian Niculescu

  Laboratoire des signaux et systèmes

  36 shared

• M. Vidyasagar

  Indian Institute of Technology Hyderabad

  33 shared

• Julio Sáez-Rodríguez

  Heidelberg University

  30 shared

• Kasper Lage

  Novo Nordisk (United States)

  20 shared

• Delia D’Avola

  15 shared

• Hitay Özbay

  Bilkent University

  15 shared

• Josep M. Llovet

  Icahn School of Medicine at Mount Sinai

  15 shared

Education

• B.S., Mathematics

  Middle East Technical University

• B.S., Electrical and Electronics Engineering

  Middle East Technical University

• M.S., Electrical and Electronics Engineering

  Bilkent Üniversitesi

• Ph.D., Bioengineering

  University of Texas at Dallas

  2015

Awards & honors

• Young Researcher Award