Sibel Çimen

Artificial intelligence (AI) is increasingly used in diagnosis, clinical decision support, patient monitoring, and treatment planning, but its integration complicates the allocation of legal responsibility when patients are harmed. This article examines whether the legal frameworks of Türkiye and the European Union adequately protect patients where clinical decisions are shaped by AI systems. Using comparative doctrinal legal analysis, it evaluates professional negligence, institutional duties, product liability, data protection, informed consent, and evidentiary barriers. It argues that physician-centred liability is insufficient when relevant knowledge, control, and preventive capacity are distributed among clinicians, healthcare institutions, technology providers, and regulators. The article develops the Distributed Accountability Framework for AI-Driven Healthcare (DAF-AIH), which maps responsibility across four dimensions—clinical, institutional, technological, and regulatory—and identifies meaningful human oversight, patient-centred transparency, and effective access to evidence as cross-cutting safeguards. The framework does not create automatic collective liability; it offers a structured method for tracing legally relevant duties, control, omissions, and causal contribution. The comparison highlights both the regulatory resources and unresolved remedial gaps in Türkiye and the EU.

This study details the development of a remote patient monitoring system with a primary focus on a novel, customized Deep Neural Network (DNN) for arrhythmia detection. The system integrates hardware for real-time data collection from biomedical sensors, where IoT-based sensor data is collected and encrypted in a central database for subsequent analysis. The novelty of the work lies in the proposed AI-based software component rather than the hardware assembly, which utilizes accessible components. The developed system is designed to function as a decision support system for healthcare personnel, providing necessary information and alerts through mobile and desktop interfaces. Data obtained from the patient is classified using the proposed deep learning method, and a detailed summary is presented. The customized DNN-based model demonstrated a test accuracy of 99.94%, with a recall of 99.92% and a precision of 99.57%, results which indicate a strong potential for clinical application due to very low false positive and false negative rates. Based on this high accuracy, the model’s outputs have been integrated into user-friendly interfaces to assist healthcare personnel. It is therefore suggested that the patient monitoring system, featuring this high-performance classification model, has the potential to contribute to the early and more reliable detection of significant diseases such as heart abnormalities and arrhythmia.

Parkinson’s disease is one of the neurodegenerative disorders that significantly affect human health. Patients experience various negative effects such as tremors, walking disorders, and impaired speech. The disease also causes instability in walking, leading to tremors, and affects their writing skills. Studies on detection of disease generally focus on speech analysis. However, PD can be diagnosed by exploiting the loss of motor ability. In this work, a data set which was recorded at Cerrahpasa Faculty of Medicine, Istanbul University is considered. The data were collected from 15 healthy subjects and 75 with Parkinson’s Disease.by a graphic tablet. Each subject asked to draw a spiral in two different conditions which are named as static spiral test (SST) and dynamic spiral test (DST) respectively, and the drawings transformed into X, Y and Z axis of movement, Grip Angle, and Pressure data. During the study, the effectiveness of SST and DST conditions are considered. Various machine learning algorithms have been tested to determine the best classifier. The effect of features was also considered by utilizing a feature elimination process. As a result, the best classification performance was obtained as 93.55% by using Kernel Naïve Bayes network with SST data, by omitting Z axis.

Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community.

Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community.

Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community.

Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitates expert labor. To facilitate myelin annotation, we developed a workflow and a software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, we shared a set of myelin ground truths annotated using this workflow.

Myelin sheath, wrapped around axons, allows rapid neural signal transmission, and degeneration of myelin causes various neurodegenerative diseases, such as, Multiple Sclerosis (MS). For candidate drug discovery, it is essential to quantify myelin. This requires tedious expert labor comprising myelin labelling on microscopic fluorescence images, usually acquired by confocal microscopes. In this study, semantic segmentation based automatic myelin segmentation on fluorescence microscopy images was introduced. Three-channel and three-dimensional fluorescence images of mouse stem cell derived neuron and oligodendrocyte co-cultures were labeled by an expert. The images were divided into patches for training and the labels corresponded to each patch were acquired. A data set of 11552 patches was used for training to identify myelin and non-myelin regions. In the data set, myelin detection performances of semantic segmentation technique were evaluated using 3 different learning algorithms. The highest accuracy value of 97.32 percent was achieved by using “RMSprop” learning algorithm with a group size of 8 and after 250 epochs. Results suggested that the proposed myelin segmentation method was suitable for detecting myelin. Thus, the outlined myelin segmentation method has the potential to be incorporated into remyelination drug screens.

Oligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors’ knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.