Search for: All records

(TSFAM) model, an adaptive human-AI teaming framework designed to enhance hard-to-place kidney acceptance decision-making by integrating transplant surgeons’ individualized expertise with advanced AI analytics (Figure 1). Methods: TSFAM is an innovative solution for complex issues in kidney transplant decision-making support. It employs fuzzy associative memory to capture and codify unique decision-making rules of transplant surgeons. Using the Deceased Donor Organ Assessment (DDOA) and Final Acceptance AI models designed to evaluate hard-to-place kidneys, TSFAM integrates fuzzy logic with deep learning techniques to manage inherent uncertainties in donor organ assessments. Surgeon-specifi c ontologies and membership functions are extracted through interviews. Similar to how a pain scale is used for understanding patients, an ontology ambiguity scale is used to develop surgeon rules (Figure 2). Fuzzy logic captures ambiguity and enables the model to adapt to evolving clinical, environmental, and policy conditions. The structured incorporation of human expertise ensures decision support remains closely aligned with local clinical practices and global best evidence. Results: This novel framework incorporates human expertise into AI decisionmaking tools to support donor organ acceptance in transplantation. Integrating surgeon-defi ned criteria into a robust decision-support tool enhances accuracy and transparency of organ allocation decision-making support. TSFAM bridges the gap between data-driven models and nuanced judgment required in complex clinical scenarios, fostering trust and promoting responsible AI adoption. Conclusions: TSFAM fuses deep learning analytics with subtleties of human expertise for a promising pathway to improve decision-making support in transplant surgery. The framework enhances clinical assessment and sets a precedent for future systems prioritizing human-AI collaboration. Prospective studies will focus on clinical implementation with dynamic interfaces for a more patient-centered, evidencebased model in organ transplantation. The intent is for this approach to be adaptable to individual case scenarios and the diverse needs of key transplant team members 

Modern kidney placement incorporates several intelligent recommendation systems which exhibit social discrimination due to biases inherited from training data. Although initial attempts were made in the literature to study algorithmic fairness in kidney placement, these methods replace true outcomes with surgeons’ decisions due to the long delays involved in recording such outcomes reliably. However, the replacement of true outcomes with surgeons’ decisions disregards expert stakeholders’ biases as well as social opinions of other stakeholders who do not possess medical expertise. This paper alleviates the latter concern and designs a novel fairness feedback survey to evaluate an acceptance rate predictor (ARP) that predicts a kidney’s acceptance rate in a given kidneymatch pair. The survey is launched on Prolific, a crowdsourcing platform, and public opinions are collected from 85 anonymous crowd participants. A novel social fairness preference learning algorithm is proposed based on minimizing social feedback regret computed using a novel logit-based fairness feedback model. The proposed model and learning algorithm are both validated using simulation experiments as well as Prolific data. Public preferences towards group fairness notions in the context of kidney placement have been estimated and discussed in detail. The specific ARP tested in the Prolific survey has been deemed fair by the participants. 

The role of human-machine teams in society is increasing, as big data and computing power explode. One popular approach to AI is deep learning, which is useful for classification, feature identification, and predictive modeling. However, deep learning models often suffer from inadequate transparency and poor explainability. One aspect of human systems integration is the design of interfaces that support human decision-making. AI models have multiple types of uncertainty embedded, which may be difficult for users to understand. Humans that use these tools need to understand how much they should trust the AI. This study evaluates one simple approach for communicating uncertainty, a visual confidence bar ranging from 0-100%. We perform a human-subject online experiment using an existing image recognition deep learning model to test the effect of (1) providing single vs. multiple recommendations from the AI and (2) including uncertainty information. For each image, participants described the subject in an open textbox and rated their confidence in their answers. Performance was evaluated at four levels of accuracy ranging from the same as the image label to the correct category of the image. The results suggest that AI recommendations increase accuracy, even if the human and AI have different definitions of accuracy. In addition, providing multiple ranked recommendations, with or without the confidence bar, increases operator confidence and reduces perceived task difficulty. More research is needed to determine how people approach uncertain information from an AI system and develop effective visualizations for communicating uncertainty. 

Purpose of Review: A transdisciplinary systems approach to the design of an artificial intelligence (AI) decision support system can more effectively address the limitations of AI systems. By incorporating stakeholder input early in the process, the final product is more likely to improve decision-making and effectively reduce kidney discard. Recent Findings: Kidney discard is a complex problem that will require increased coordination between transplant stakeholders. An AI decision support system has significant potential, but there are challenges associated with overfitting, poor explainability, and inadequate trust. A transdisciplinary approach provides a holistic perspective that incorporates expertise from engineering, social science, and transplant healthcare. A systems approach leverages techniques for visualizing the system architecture to support solution design from multiple perspectives. Summary: Developing a systems-based approach to AI decision support involves engaging in a cycle of documenting the system architecture, identifying pain points, developing prototypes, and validating the system. Early efforts have focused on describing process issues to prioritize tasks that would benefit from AI support.