Energy consumption optimisation has emerged as a critical need in Autonomous Mobile Robots (AMRs). Conventional A* implementations typically minimise path distance, neglecting energy-relevant factors such as directional changes and trajectory smoothness that significantly impact battery life and operational costs. This work proposes two energy-aware A* variants trained on empirical data from the KUKA KMP 1500 platform, where energy consumption is measured as battery SoC depletion: A*-RF, which integrates a Random Forest (RF) model directly into the cost function, and A*-MOD, which approximates the energy model through RF feature importance weights, achieving linear computational complexity O(nf). The RF model predicted energy consumption with an RMSE below 1.5% relative error, identifying travel distance and rotation angle as the dominant energy factors. Experimental validation across 42 path planning scenarios on a real industrial factory floor demonstrates that A*-MOD reduces energy consumption by up to 58.91% and improves operational autonomy by 2.21 times, with statistically significant improvements (p < 0.01) across all evaluated metrics.

This study examined how healthcare professionals (HCP) perceive and trust AI in clinical settings, and what kinds of explanations they need to use it effectively. Using interviews with six HCPsand a questionnaire ($N=41$), the findings show that trust was higher for administrative tasks and lower for direct clinical decisions, regardless of their prior AI or clinical experience or AI literacy. Clinical validation and algorithmic bias ranked above explainability as trust factors, indicating that explainability is not a primary trust-building mechanism. However, HCP consistently demanded explanations as a tool to support their own clinical reasoning, preferring to receive them across all clinical contexts rather than only under disagreement with the system, and valued grounding in medical evidence and consistency with clinical protocols over clarity or simplicity. These findings argue for an HCXAI design approach that treats explainability as a critical-reasoning tool rather than a primary trust mechanism.