Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients’ outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. This article explores the diverse applications and reviews the current state of AI adoption in healthcare. It concludes by emphasizing the need for collaboration between physicians and technology experts to harness the full potential of AI.

Background: The integration of artificial intelligence (AI) into various fields has ushered in a new era of multidisciplinary progress. Defined as the ability of a system to interpret external data, learn from it, and adapt to specific tasks, AI is poised to revolutionize the world. In forensic medicine and pathology, algorithms play a crucial role in data analysis, pattern recognition, anomaly identification, and decision making. This review explores the diverse applications of AI in forensic medicine, encompassing fields such as forensic identification, ballistics, traumatic injuries, postmortem interval estimation, forensic toxicology, and more. Results: A thorough review of 113 articles revealed a subset of 32 papers directly relevant to the research, covering a wide range of applications. These included forensic identification, ballistics and additional factors of shooting, traumatic injuries, post-mortem interval estimation, forensic toxicology, sexual assaults/rape, crime scene reconstruction, virtual autopsy, and medical act quality evaluation. The studies demonstrated the feasibility and advantages of employing AI technology in various facets of forensic medicine and pathology. Conclusions: The integration of AI in forensic medicine and pathology offers promising prospects for improving accuracy and efficiency in medico-legal practices. From forensic identification to post-mortem interval estimation, AI algorithms have shown the potential to reduce human subjectivity, mitigate errors, and provide cost-effective solutions. While challenges surrounding ethical considerations, data security, and algorithmic correctness persist, continued research and technological advancements hold the key to realizing the full potential of AI in forensic applications. As the field of AI continues to evolve, it is poised to play an increasingly pivotal role in the future of forensic medicine and pathology.

Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Wound healing is a complex, tightly regulated process essential for maintaining skin barrier function. Chronic wounds, often complicated by biofilm-forming bacteria and elevated oxidative stress, pose significant challenges in clinical management. The rise of antibiotic-resistant bacteria has further exacerbated the problem, limiting therapeutic options and complicating wound treatment. Traditional wound care approaches frequently fail to provide real-time accurate insights into wound status, leading to delayed or suboptimal treatments. Recent advancements in modern and smart wound dressings, which integrate various biosensors, different new drug delivery systems, and wireless communication technology, offers promising solutions for monitoring wound progression over time. These innovations enable early detection of adverse events such as bacterial infections and inflammation, facilitating more effective, on-demand treatment. This review highlights the current state of antibiotic-embedded wound dressings, discusses their limitations, and explores the potential of next-generation wound dressings incorporating microelectronic sensors for real-time monitoring and adaptive therapeutic responses to support healing and combat antimicrobial resistance.

Cancer is a leading cause of death globally, claiming millions of lives each year. Despite the availability of numerous anticancer drugs, the need for new treatment options remains essential. Many current therapies come with significant toxicity, lead to various side effects, or do not consistently deliver the expected therapeutic results. Purines and pyrimidines are fundamental building blocks of nucleic acids and play crucial roles in cellular metabolism and signaling. Recent advances in medicinal chemistry have led to the development and synthesis of various derivatives that exhibit selective cytotoxic effects against cancer cells while minimizing toxicity to healthy tissues. Purine and pyrimidine scaffolds, due to their well-established biological roles and structural versatility, have emerged as key pharmacophoric fragments in anticancer drug discovery. In recent years, the rational design of hybrid molecules incorporating these heterocycles has shown promise in overcoming drug resistance, improving target selectivity, and enhancing pharmacological profiles. Purine and pyrimidines scaffolds hold significant potential as foundations for novel antitumor drugs, with established representatives in cancer treatment, including 5-fluorouracil, cladribine, capecitabine, and several others. In addition, the article discusses the challenges and future developments of purine and pyrimidine derivatives and hybrid molecules as antitumor drugs and emphasizes the need for continued research to optimize their effectiveness and reduce side effects. Overall, the innovative use of these compounds represents a major advance in targeted cancer therapy and holds promise for improving the therapeutic efficacy of malignant diseases.

The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences of the COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications in risk prediction and diagnosis of heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts on AI and machine learning (ML) are explained to provide a foundational understanding for those seeking knowledge, supported by examples from the literature and current practices. We analyze AI and ML methods for arrhythmias, heart failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, hypertension, pulmonary embolism, and myocardial infarction, comparing their effectiveness from both medical and AI perspectives. Special emphasis is placed on AI applications in COVID-19 and cardiology, including detailed comparisons of different methods, identifying the most suitable AI approaches for specific medical applications and analyzing their strengths, weaknesses, accuracy, clinical relevance, and key findings. Additionally, we explore AI’s role in the emerging field of cardio-oncology, particularly in managing chemotherapy-induced cardiotoxicity and detecting cardiac masses. This comprehensive review serves as both an insightful guide and a call to action for further research and collaboration in the integration of AI in cardiology, aiming to enhance precision medicine and optimize clinical decision-making.