With healthcare staff shortages in hospitals, there is a pressing need to fill critical gaps in patient care across the United States. One potential ally in this effort is artificial intelligence (AI); however, its practical implementation in clinical settings remains under evaluation. In the study conducted by Rachel Chacko et al., researchers examined how integrating AI technologies—specifically basal cell carcinoma (BCC) detection and tumor mapping—could impact the efficiency of clinical and laboratory staff. The study was performed on 104 consecutive Mohs micrographic surgeries (MMS) over 20 days, assessing algorithm integration using three key measures: slide waiting time, staff waiting time, and histotechnician waiting time. The BCC detection algorithm was implemented immediately upon slide placement beside the microscope. After comparing actual and simulated slide waiting times, time saved or lost was calculated to quantify the benefit of algorithm integration. The simulated results revealed a reduction of 35.6% in slide waiting time, 18.6% in histotechnician waiting time, and 18.4% in staff waiting time per day. These findings demonstrate that implementing AI could save 20–30 minutes daily, allowing healthcare workers to review additional cases and improve overall efficiency. However, the authors emphasized that implementation should remain non-autonomous to maintain quality outcomes and that widespread use may be limited by the high computational requirements of such systems.