Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field.

Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.

Clinical Evaluation of Proline, Glutamic acid, and Leucine-Rich Protein 1 Expression in Astrocytomas and Correlations with the Proliferation Marker Ki-67.

Malignant astrocytomas presenting in humans of any age group are a challenge to diagnose and treat. Hence, there is a quest for new markers to ascertain their grades and predict disease outcomes. Proline, glutamic acid, and leucine-rich protein 1 (PELP1), a nuclear receptor co-regulator, is an oncogene found in various cancers. We postulate that by screening for PELP1, its correlation with survival outcomes of patients across various grades can indicate a plausible novel diagnostic marker and a potential therapeutic target in gliomas. Immunostaining of 100 cases of astrocytomas for PELP1 was performed on paraffin-embedded sections. Results showed that PELP1 expression increases with higher grades; the mean H-score of PELP1 in grade-I astrocytomas was determined to be 112.3, whereas in grade-IV it was 235.1 (P value = 0.0001). Survival analysis of patients with H-score of 200-300 was only 8.8% and 68.8% in patients with scores of 0-100. PELP1 expression in high-grade astrocytomas is an important factor in determining the outcomes. Graphical abstract Evaluation of molecular expression of PELP1 along with Ki-67 LI signifies a linear increase in its expression pattern among different grades of astrocytomas from low- to high-grade tumors, which can serve as a potential prognostic molecular marker in differentiating various types of astrocytomas in humans.