Artificial Intelligence (AI) is rapidly advancing and is poised to transform healthcare. This thesis explores the current and future role of AI in medicine, radiology, and mammography, as well as the bioethical considerations surrounding its use. The introduction provides an overview of AI and its applications in medicine, followed by a discussion of how AI is being used in clinical care including its potential for improving patient outcomes and reducing healthcare costs. The thesis then delves into AI in radiology, specifically its use in image interpretation, diagnosis, triaging, and treatment planning. The role of AI in mammography is also explored, focusing on its potential for improving the accuracy of breast cancer detection and diagnosis, as well as the aspect of patient communication and education. The future of AI in healthcare is also discussed including potential challenges such as the need for high quality unbiased data and the ethical considerations surrounding AI’s use. The bioethical considerations surrounding AI in healthcare are explored including issues related to privacy, autonomy, and bias. Finally, the thesis concludes with a discussion of what can be expected from the future of AI in medicine and the implications for healthcare professionals, patients, and society. In summary, this thesis provides a comprehensive overview of the current and future role of AI in medicine, radiology, mammography, and patient care while highlighting the importance of addressing bioethical considerations as this technology continues to evolve and make its way into our lives. / Urban Bioethics

Artificial Intelligence (AI) has the capability to revolutionize modern life. From humble beginnings of simple machines to current day programs capable of winning “Jeopardy!” and passing medical board exams, the applications of this maturing technology are incredibly diverse. Healthcare in particular contains many inefficiencies and opportunities for improvement for which AI programs have shown encouraging results. However, the ramifications of extensive implementation are unclear. In order to cultivate innovative technology safely, the core ethical principles of beneficence, non-maleficence, autonomy and justice must be prioritized. / Urban Bioethics

<p dir="ltr">Impaired cerebral vasoregulation is one of the most common post-ischemic stroke effects. Diagnosis and prevention of this condition is often invasive, costly and in-effective. This impairment restricts the cerebral blood vessels to properly regulate blood flow, which is very important for normal brain functioning. Developing accurate, non-invasive and efficient methods to detect this condition aids in better stroke diagnosis and prevention. </p><p dir="ltr">The aim of this thesis is to develop deep learning techniques for the purpose of detection of cerebral vasoregulation impairments by analyzing physiological signals. This research employs various Deep learning techniques like Convolution Neural Networks (CNN), MobileNet, and Long-Short-Term Memory (LSTM) to determine variety of physiological signals from the PhysioNet database like Electrocardio-gram (ECG), Transcranial Doppler (TCD), Electromyogram (EMG), and Blood Pressure(BP) as stroke or non-stroke subjects. The effectiveness of these algorithms is demonstrated by a classification accuracy of 90\% for the combination of ECG and EMG signals. </p><p dir="ltr">Furthermore, this research explores the importance of analyzing dynamic physiological activities in determining the impairment. The dynamic activities include Sit-stand, Sit-stand-balance, Head-up-tilt, and Walk dataset from the PhysioNet website. CNN and MobileNetV3 are employed in classification purposes of these signals, attempting to identify cerebral health. The accuracy of the model and robustness of these methods is greatly enhanced when multiple signals are integrated. </p><p dir="ltr">Overall, this study highlights the potential of deep multimodal physiological learning in the development of precision brain medicine further enhancing stroke diagnosis. The results pave the way for the development of advanced diagnostic tools to determine cerebral health. </p>