AI is transforming healthcare by improving diagnoses, therapy and care of patients. However, AI is susceptible to bias through unbalanced training datasets, algorithmic flaws and healthcare system inequities. The bias can translate into unequal healthcare decisions that disproportionately affect marginalized groups of people. To address this, diverse, well-curated datasets and bias-detecting training processes like debiasing through adversarial processes are needed. Explainable AI models and ongoing fairness audits ensure accountability of these processes. Ethical guidelines need to ensure protection of patient confidentiality and informed consent. If left unchecked, AI bias can perpetuate healthcare inequities instead of resolving them. By combining engineering solutions with ethical oversight, medical professionals can create fairer AI-driven tools that benefit all patients. This article explores the challenges of bias in medical AI and strategies to make these technologies more equitable for diverse populations.

We explored how three popular tools—Blender, Unity and Unreal Engine—can help scientists create detailed 3D visuals of cells and biological processes. Our study highlights how Blender excels in making highly accurate static models, Unity supports real-time simulations and Unreal Engine provides stunning visual quality for large-scale data. By comparing these platforms, we aim to guide researchers in choosing the right tool for their projects. This work emphasizes how 3D visualization can make complex biological concepts more accessible, improving both research and education.