3066-280XISSN (Online)
Volume 4 , Issue 2 , PP: 33-41, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
P. K. Dutta 1 *
Doi: https://doi.org/10.54216/MOR.040204
We should subject artificial intelligence (AI) to neurological diagnostics for detailed ethical consideration and examination of compliance questions. When applied to neuroimaging, these AI technologies improve diagnostic performance and treatment planning; however, they give rise to issues such as algorithmic bias, data privacy, and the intelligibility of resulting AI-generated insights. The issue of bias is related to the necessity of obtaining informed consent because of using patient data for training models of AI, which in turn will create more problems since the machine learning process will be based on data that is itself bigoted. In addition, the self-governing characteristic of AI systems creates additional concerns regarding responsibility for misuse; it is still unclear who is to blame when an AI system commits an obvious mistake, like misdiagnosis or incorrect treatment. Governance structures must adapt to these questions to guarantee that healthcare AI is ethically upraised, transparent, and fair. This review underscores the importance of interprofessional relationships between researchers and scholars, clinicians and practitioners, and ethicists when dealing with these issues. As social safeguards, demographic benchmarks and best practices have to be set, it enables the medical field to benefit from the opportunities provided by AI in neurological diagnostics and uphold the patient's respect for their rights while pushing for equal access to equal quality health care. Lastly, it becomes imperative to counter these ethical questions, which is imperative for the effectiveness of AI technologies and for building public acceptance of this technology in clinical practice.
Ethical challenges , Regulatory compliance , AI technologies , Neurological diagnostics , Algorithmic bias , Data privacy
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