Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.

Our objective in this study was to promote the growth of bone cells on biomedical titanium (Ti) implant surfaces via surface modification involving sandblasting, alkaline etching, and type I collagen immobilization using the natural cross-linker genipin. The resulting surface was characterized in terms topography, roughness, wettability, and functional groups, respectively using field emission scanning electron microscopy, 3D profilometry, and attenuated total reflection-Fourier transform infrared spectroscopy. We then evaluated the adhesion, proliferation, initial differentiation, and mineralization of human bone marrow mesenchymal stem cells (hMSCs). Results show that sandblasting treatment greatly enhanced surface roughness to promote cell adhesion and proliferation and that the immobilization of type I collagen using genipin enhanced initial cell differentiation as well as mineralization in the extracellular matrix of hMSCs. Interestingly, the nano/submicro-scale pore network and/or hydrophilic features on sandblasted rough Ti surfaces were insufficient to promote cell growth. However, the combination of all proposed surface treatments produced ideal surface characteristics suited to Ti implant applications.

Although polyetheretherketone (PEEK) is becoming more widely used in dentistry applications, little is known about how aging will affect this material. Therefore, this study aimed to investigate the influence of an aging treatment on fracture characteristics of PEEK dental crowns. Additionally, the impact of the addition of titanium dioxide (TiO₂) into PEEK was examined. Two types of commercial PEEK discs were used in this study, including TiO₂-free and 20% TiO₂-containing PEEK. The PEEK dental crowns were fabricated and aging-treated at 134 °C and 0.2 MPa for 5 h in accordance with the ISO 13356 specification before being cemented on artificial tooth abutments. The fracture loads of all crown samples were measured under compression tests. Results demonstrated that adding TiO₂ enhanced the fracture load of PEEK crowns compared to TiO₂-free PEEK crowns before the aging treatment. However, the aging treatment decreased the fracture load of TiO₂-containing PEEK crowns while increasing the fracture load of TiO₂-free PEEK crowns. The fracture morphology of TiO₂-containing PEEK crowns revealed finer feather shapes than that of the TiO₂-free PEEK crowns. We concluded that adding TiO₂ increased the fracture load of PEEK crowns without aging treatment. Still, the aging treatment influenced the fracture load and microscopic fracture morphology of PEEK crowns, depending on the addition of TiO₂.