FW-HTF-RM: Measuring learning gains in man-machine assemblage when augmenting radiology work with artificial intelligence

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The work setting of the future presents an opportunity for human-technology partnerships, where a harmonious connection between human-technology produces unprecedented productivity gains. A conundrum at this human-technology frontier remains - will humans be augmented by technology or will technology be augmented by humans? This project overcomes the conundrum of human and machine as separate entities and instead, treats them as an assemblage. As groundwork for the harmonious human-technology connection, this assemblage needs to learn to fit synergistically. This learning is called assemblage learning and it will be important for Artificial Intelligence (AI) applications in health care, where diagnostic and treatment decisions augmented by AI will have a direct and significant impact on patient care and outcomes. This project will also identify ways in which learning can be shared between assemblages, such that collective swarms of connected assemblages can be created. The project will create a new learning model that integrates and measures concepts from individuals learning to swarm learn. The project will help demonstrate a symbiotic learning assemblage, such that envisioned productivity gains from AI can be achieved without loss of human jobs. Even though the focus is on visual cognitive tasks in radiology, lessons from this project may be applicable to other domains where human intelligence will be augmented by machine intelligence.Recent studies of human versus machine competitions have demonstrated that assemblages that combine human-technology partnerships are stronger than individual humans or machines. By building on these, this project will integrate state-of-the-art algorithms into the radiology workflow. The project will answer the following research questions: Q1: How to develop assemblages, such that human-technology partnerships produce a "good fit" for visually based cognition-oriented tasks in radiology? Q2: What level of training should pre-exist in the individual human (radiologist) and independent machine learning model for human-technology partnerships to thrive? Q3: Which aspects and to what extent does an assemblage learning approach lead to reduced errors, improved accuracy, faster turn-around times, reduced fatigue, improved self-efficacy, and resilience? A rigorous counterbalanced trial will be performed to assess individual radiologists interpreting images with and without the assemblage. Data on clinician engagement from EHR systems will be captured and analyzed, along with pre-test and post-test surveys and interviews. Deep and wide analysis of the quantitative and qualitative data from the trial will answer questions related to learning gains, task performance, emotional as well as behavioral aspects of learning in an assemblage. The project employs perspectives from Science & Technology Studies, Computer Science, Psychology, and Learning Sciences, to create and study assemblages that can produce gains in routine radiology work.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.