Epistemological Approach to the “Visual Perception” Concept Applied to Medicine
Abdullah Emre Taçyıldız1*, Melih Üçer2
1Department of Neurosurgery, Karabük University Faculty of Medicine, Karabük, Turkey
2Department of Neurosurgery, Biruni University Faculty of Medicine, Istanbul, Turkey
Objective: This study aims to analyze the limitations of human visual perception, compare them with the visual adaptations of other species, and explore how technological advancements—such as artificial intelligence, medical imaging, and augmented reality—have improved medical diagnostics and surgical precision.
Methods: A structured literature review was conducted, incorporating comparative analyses of visual perception across species, including frogs, dragonflies, and humans. The study evaluated the role of advanced imaging technologies, artificial intelligence-based diagnostics, and digital image processing tools in overcoming the limitations of biological vision. Selection criteria for species comparisons were based on functional adaptations and their relevance to medical imaging applications.
Results: Findings indicate that species-specific visual adaptations are optimized for survival rather than accuracy. Human vision, while advanced, is inherently constrained by physiological and neurological limitations. However, medical imaging technologies, including fluorescence-guided surgery, histology, and AI-assisted diagnostics, have significantly enhanced the precision of visual interpretation in clinical settings. The integration of computational tools, such as Adobe Photoshop for forensic and radiological analysis, further refines image accuracy and medical decision-making.
Conclusion: The interplay between biological vision and technological advancements underscores the necessity of integrating artificial intelligence and advanced imaging in medical practice. Future research should focus on optimizing these technologies to further enhance diagnostic accuracy, surgical precision, and clinical outcomes, ultimately pushing the boundaries of human visual perception.
DOI: 10.29245/2572.942X/2025/1.1309 View / Download PdfEnhancing Intracranial Aneurysm Detection with Artificial Intelligence in Radiology
Ilya Adamchic
Department of Radiology, Vivantes Hospital im Friedrichshain, Berlin, Germany
Intracranial aneurysms (IAs) pose a significant public health challenge due to their potential for rupture and associated morbidity and mortality. Despite advancements in imaging technologies such as magnetic resonance angiography (MRA) and computed tomography angiography (CTA), detecting small, incidental IAs remains challenging, particularly amid increasing global imaging volumes and resource constraints. Artificial intelligence (AI) has emerged as a transformative tool in medical imaging, demonstrating potential to enhance diagnostic accuracy and efficiency. Deep learning models, particularly convolutional neural networks (CNNs), have shown near-expert accuracy in detecting subtle aneurysmal features, enabling early diagnosis and improving clinical workflows. AI-driven approaches extend beyond detection to include rupture risk assessment, predictive diagnostics, and treatment planning, thereby improving patient-specific care and reducing unnecessary interventions.
However, challenges such as false-positive rates, ethical considerations, and the need for robust validation studies hinder AI adoption in clinical practice. This review contextualizes recent advancements and the findings of Adamchic et al. (2024), within the broader landscape of AI applications in IA diagnostics. It discusses AI’s role in addressing diagnostic variability, mitigating radiologist workloads, and enhancing reproducibility, particularly for junior clinicians. The article also explores barriers to widespread implementation, including data safety, algorithm transparency, and financial constraints, while emphasizing the need for collaborative efforts to refine AI models and integrate them seamlessly into radiology workflows. By addressing these challenges, AI has the potential to revolutionize intracranial aneurysm management, improving patient outcomes and transforming modern radiology practices.
DOI: 10.29245/2572.942X/2025/1.1310 View / Download PdfNanomedicine: Transforming Neurological Therapies and Precision Medicine
Huma Ikram
Neurochemistry and Biochemical Neuropharmacology Research Unit, Department of Biochemistry, University of Karachi, Karachi, Pakistan
Nanomedicine is going to be a novel and different avenue of therapy for the neurological patients, overcoming blood-brain barrier difficulties while also addressing the unique complexity of neurological diseases. With applications of nanoparticles, including liposomes, dendrimers, exosomes, and polymeric nanoparticles, effective drug delivery will be possible via targeted and controlled release and reduced adverse effects. These technological breakthroughs are going to be of great help in the treatment of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's, and in conditions like multiple sclerosis. Because of this, nanomedicine has a great promise in the field of neuroinflammatory conditions as well. In addition, nanomedicine is going to play a significant role in precision medicine, where therapies can be planned according to the genetic, molecular, and biological makeup of the patients. However, precision in diagnosis and monitoring will also provide a personalized treatment regimen as nanotechnology can be integrated with other advanced diagnostic technologies like MRI, PET, and biomarker detection. Although it has a lot of promise, clinical translation has not avoided the same problems that have proved to be highly disconcerting for so many developments in nanomedicine, including issues of biocompatibility, safety, and, in the case of some products, regulatory approval. Addressing these factors will prove critical for the successful embedding of nanomedicine into the routine neurological care system. This review touches on the current status of nanomedicine in neurology, the promise that it holds for precision treatments, and the barriers that apply to its ability to effect real clinical practice portrayals, thereby providing an insight into the future of personalized therapy in neurology.
DOI: 10.29245/2572.942X/2025/1.1311 View / Download PdfCommentary: T1 Mapping from routine 3D T1-weighted inversion recovery sequences in clinical practice: comparison against reference inversion recovery fast field echo T1 scans and feasibility in multiple sclerosis
Vivian S. Nguyen1*, Griffin J. Young2, Adil Javed1, Timothy J. Carroll2
1Department of Neurology, The University of Chicago, Chicago, IL, USA
2Department of Radiology, The University of Chicago, Chicago, IL, USA
MRI has long been a critical tool for diagnosing and monitoring Multiple Sclerosis (MS). Conventional MRI employed in clinical practice is non-parametric, which disallows quantitative measures of tissue damage. This creates an unmet need to develop a post-acquisition image processing algorithm that can convert a qualitative image into a corresponding quantitative map. We present a methodology that can convert a clinically routine T1-weighted MPRAGE image into a parametric T1 map with high accuracy and precision in relation to a commonly used T1 mapping reference standard. We evaluate the methodology’s performance in Multiple Sclerosis for the purpose of quantifying tissue damage primarily in lesions and secondarily in white matter and gray matter regions.
DOI: 10.29245/2572.942X/2024/3.1305 View / Download PdfA Brief Review on Clinical Treatment of Oculogyric Crisis
Liping Wu, Tao Lv*
The People’s Hospital of Deyang, North Taishan Road, Deyang, Sichuan, China
As of now, there are increasing cases related to oculogyric crisis (OGC), mainly focusing on clinical manifestations. The pathogenesis of OGC is still uncertain, and there is no unified treatment protocol. This review aims to explore the treatment and management strategies for OGC based on existing cases, hoping to provide references for clinicians in identifying and treating OGC in practice.
DOI: 10.29245/2572.942X/2024/3.1304 View / Download Pdf