This paper evaluates the ethical implications of utilizing artificial intelligence (AI) algorithms in neurological diagnostic examinations. Applications of AI technology have been utilized to aid in the determination of pharmacological dosages of gadolinium for brain lesion detection, localization of seizure foci, and the characterization of large vessel occlusion in ischemic stroke patients. Multiple subtypes of AI- machine learning (AI/ML) algorithms are analyzed as AI-assisted neurology utilizes supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learning models. As ANN and DNN analyses can be applied to data with an unknown clinical diagnosis, these algorithms are evaluated according to Bayesian statistical analyses. Bayesian neural network analyses are incorporated as these algorithms indicate that the predictive accuracy and model performance are dependent upon accurate configurations of the model's hyperparameters and neural inputs. Thus, mathematical evaluations of AI algorithms are comprehensively explored to examine their clinical utility as underperformance of AI/ML models may have deleterious consequences that affect patient outcomes due to misdiagnosis and false-negative test results.

中文翻译：

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神经诊断中人工智能和机器学习的评估

本文评估了在神经诊断检查中使用人工智能 (AI) 算法的伦理影响。人工智能技术的应用已被用来帮助确定钆的药理剂量，用于脑病变检测、癫痫病灶定位以及缺血性中风患者大血管闭塞的表征。由于人工智能辅助神经病学利用监督、无监督、人工神经网络 (ANN) 和深度神经网络 (DNN) 学习模型，因此分析了人工智能机器学习 (AI/ML) 算法的多个子类型。由于 ANN 和 DNN 分析可应用于临床诊断未知的数据，因此这些算法根据贝叶斯统计分析进行评估。贝叶斯神经网络分析被纳入，因为这些算法表明预测准确性和模型性能取决于模型超参数和神经输入的准确配置。因此，人工智能算法的数学评估被全面探索，以检查其临床效用，因为人工智能/机器学习模型的表现不佳可能会产生有害后果，由于误诊和假阴性测试结果而影响患者的治疗结果。