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Artificial intelligence (AI) tools are designed to improve the efficacy and efficiency of data analysis and interpretation by the human decision maker. However, we know little about the optimal ways to present AI output to providers. This study used radiology image interpretation with AI-based decision support to explore the impact of different forms of AI output on reader performance. Readers included 5 experienced radiologists and 3 radiology residents reporting on a series of COVID chest x-ray images. Four different forms (1 word summarizing diagnoses (normal, mild, moderate, severe), probability graph, heatmap, heatmap plus probability graph) of AI outputs (plus no AI feedback) were evaluated. Results reveal that most decisions regarding presence/absence of COVID without AI were correct and overall remained unchanged across all types of AI outputs. Fewer than 1% of decisions that were changed as a function of seeing the AI output were negative (true positive to false negative or true negative to false positive) regarding presence/absence of COVID; and about 1% were positive (false negative to true positive, false positive to true negative). More complex output formats (e.g., heat map plus a probability graph) tend to increase reading time and the number of scans between the clinical image and the AI outputs as revealed through eyetracking. The key to the success of AI tools in medical imaging will be to incorporate the human into the overall process to optimize and synergize the human-computer dyad, since at least for the foreseeable future, the human is and will be the ultimate decision maker. Our results demonstrate that the form of the AI output is important as it can impact clinical decision making and efficiency.