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Healthcare AI software systems raise ethical concerns that are often discussed as high-level principles but are difficult to translate into concrete software engineering requirements. This emerging-results paper reports preliminary findings from a multi-stakeholder interview study on how practitioners experience and prioritise ethical concerns in healthcare AI software systems. We conducted 17 semi-structured interviews with healthcare professionals (n=5), healthcare researchers (n=6), and software engineers (n=6). Using thematic synthesis, we identified three AI use contexts and four recurring challenge categories: model reliability and quality, data quality and processing, human oversight and skills gaps, and institutional and regulatory constraints. Privacy and safety emerged as baseline concerns across roles, while bias, accountability, and transparency varied depending on practitioners' responsibilities and use contexts. Based on these preliminary findings, we propose role-specific patterns as lightweight requirements artefacts to translate practitioner priorities into actionable software engineering practices across requirements elicitation, system design, testing, and auditing. We present these patterns as an initial artefact for community feedback and as a first step toward a broader approach to ethical requirements engineering (RE) for healthcare AI software systems. Future work will validate and refine the patterns using a larger, more balanced sample, evaluate their usefulness through practitioner workshops, and develop a reusable catalogue with guidance on traceability, verification, and auditing. Long-term, our goal is to support healthcare AI teams in moving from abstract ethical principles to testable requirements that can be integrated into existing development and governance workflows.