Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence (AI), allowing for image-based diagnosis on the background of digital pathology. There are efforts for developing AI-based tools to save pathologists time and eliminate errors. Here, we describe the elements in the development of computational pathology (CPATH), its applicability to AI development, and the challenges it faces, such as algorithm validation and interpretability, computing systems, reimbursement, ethics, and regulations. Furthermore, we present an overview of novel AI-based approaches that could be integrated into pathology laboratory workflows.

The clustered regularly interspaced palindromic repeat (CRISPR)-Cas system has revolutionized genetic engineering due to its simplicity, stability, and precision since its discovery. This technology is utilized in a variety of fields, from basic research in medicine and biology to medical diagnosis and treatment, and its potential is unbounded as new methods are developed. The review focused on medical applications and discussed the most recent treatment trends and limitations, with an emphasis on CRISPR-based therapeutics for infectious disease, oncology, and genetic disease, as well as CRISPR-based diagnostics, screening, immunotherapy, and cell therapy. Given its promising results, the successful implementation of the CRISPR-Cas system in clinical practice will require further investigation into its therapeutic applications.

Digital pathology offers powerful tools for biomarker discovery, analysis, and translation. Despite its advantages, the clinical adoption of digital pathology has been slow. A clinical and methodological validation is required for novel digital pathological biomarkers. Four steps are required to validate a novel pathological digital biomarker for clinical use: sample collection and processing, analytical validation, clinical validation, and clinical utility. The digital biomarkers and their diagnostic, monitoring, pharmacodynamic response, predictive, prognostic, safety, and risk assessment applications are discussed. Adopting pathological digital biomarkers can be used in conjunction with other diagnostic technologies to select the most appropriate patient treatment, thereby reducing patient suffering and healthcare costs.