Ethical Considerations in AI-Based Brain Tumour Diagnosis

Artificial intelligence has really transformed medical diagnosis in brain tumours by changing the direction of medical imaging. This study compares the performances of two AI models, YOLO and Roboflow, in the detection and classification of tumours. YOLO showed high accuracy and speed in tumour detection but faced difficulties when it came to small or irregularly shaped tumours, especially low-grade gliomas. Roboflow did very well in multi label classification which is necessary to distinguish types of tumors. That is good, for example, to differentiate between meningiomas versus pituitary tumours, whereas not so good in the case of heterogeneous gliomas. Hence, these are complementary strengths that can be combined to help improve diagnostic workflows. Besides the technical performances, important ethical challenges were pointed out such as biased dataset imbalances, risks to patient privacy, unclear accountability in the case of diagnostic errors, and opacity of model decision-making. Proposed solutions include diversification of datasets, privacy-preserving techniques such as federated learning, accountability frameworks, and explainable AI. These findings emphasize the need for a multidisciplinary approach that integrates technical innovation with ethical safeguards, ensuring the equitable and trustworthy application of AI in clinical practice, while improving diagnostic accuracy and patient outcomes in neuro-oncology.