Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review.

BACKGROUND: Navigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including the need for accurate prediction of disease progression and response to treatment. Artificial intelligence (AI) presents a transformative approach that enables the development of sophisticated predictive models and personalized treatment strategies that enhance early detection and improve therapeutic interventions for better patient outcomes. METHODS: An extensive search was conducted to retrieve relevant articles from PubMed, Scopus, and Web of Science databases up to April 24, 2023. Data were collected using a standardized extraction form, and the results are presented in tables and graphs, showing frequencies and percentages. The authors adhered to the PRISMA-ScR checklist to ensure transparent reporting of the study. RESULTS: Of the 176 articles initially identified, 12 were selected for our study after removing duplicates and applying the inclusion and exclusion criteria. AI's primary applications of AI in managing CML included tumor diagnosis/classification (n = 9, 75%), prediction/prognosis (n = 2, 17%), and treatment (n = 1, 8%). For tumor diagnosis, AI is categorized into blood smear image-based (n = 5), clinical parameter-based (n = 2), and gene profiling-based (n = 2) approaches. The most commonly employed AI models include Support Vector Machine (SVM) (n = 5), eXtreme Gradient Boosting (XGBoost) (n = 4), and various neural network methods, such as Artificial Neural Network (ANN) (n = 3). Furthermore, Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) achieved 100% accuracy and sensitivity in organizing leukemia data types, whereas MayGAN attained 99.8% accuracy and high performance in diagnosing CML from blood smear images. CONCLUSIONS: AI offers groundbreaking insights and tools for enhancing prediction, prognosis, and personalized treatment in chronic myeloid leukemia. Integrated AI systems empower healthcare practitioners with advanced analytics, optimizing patient care and improving clinical outcomes in CML management.

Dos and don'ts in designing a computerized oral and lip squamous cell cancer registry.

BACKGROUND: In the last ten years, many countries have started to develop constructive systems for registering common diseases and cancers. In this research, we intended to determine and identify the minimum data set (MDS) required for the design of the oral and lip squamous cell cancer registration system in Iran. METHODS AND MATERIAL: At first, primary information elements related to disease registries were extracted using scientific papers published in reliable databases. After reviewing the books, related main guidelines, and 42 valid articles, the initial draft of a researcher-made questionnaire was compiled. To validate the questionnaire, two focus group meetings were held with 29 expert panel members. The final version of this questionnaire was prepared by extracting different questions and categories and receiving numerous pieces of feedback from specialists. Lastly, a final survey was conducted by the experts who were present at the previous stage. RESULTS: Out of 29 experts participating in the study, 17 (58.62%) were men and 12 (40.37%) were women. The age range of experts varies from 34 to 58 years. One hundred-fourteen items, which are divided into ten main parts, were considered the main information elements of the registry design. The main minimum data sets have pertained to the demographic and clinical information of the patient, information related to the consumed drugs, initial diagnostic evaluations of the patient, biopsy, tumor staging at the time of diagnosis, clinical characteristics of the tumor, surgery, histopathological characteristics of the tumor, pathologic stage classification, radiotherapy details, follow-up information, and disease registry capabilities. The distinctive characteristics of the oral and lip squamous cell cancer registry systems, such as the title of the disease registration programme, the population being studied, the geographic extent of the registration, its primary goals, the definition of the condition, the technique of diagnosis, and the kind of registration, are all included in a model. CONCLUSION: The benefits of designing and implementing disease registries can include timely access to medical records, registration of information related to patient care and follow-up of patients, the existence of standard forms and the existence of standard information elements, and the existence of an integrated information system at the country level.

Artificial intelligence in nanotechnology for treatment of diseases.

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

Using Electronic Health Record System to Establish a National Patient's Registry : Lessons learned from the Cancer Registry in Iran.

BACKGROUND: In Iran, the Integrated Electronic Health Record system, called SEPAS, has been established to store all patient encounters of individuals referring to healthcare facilities. OBJECTIVE: We aimed to develop a model for cleaning SEPAS and applying its data in other databases. METHODS: We used cancer data from SEPAS as the sample. We developed a guideline to identify codes for cancer-related diagnoses and services in the database. Furthermore, we searched the SEPAS database based on ICD-10 and the diagnosis description in English and Farsi in an Excel sheet. We added codes and descriptions of pharmaceuticals and procedures to the list. We applied the above database and linked it to the patient records to identify cancer patients. A dashboard was designed based on this information for every cancer patient. RESULTS: We selected 5,841 diagnostic codes and phrases, 9,300 cancer pharmaceutics codes, and 452 codes from cancer-specific items related to the diagnostic procedures and treatment methods. Linkage of this list to the patient list generated a database of about 197,164 cancer patients for linkage in the registry database. CONCLUSIONS: Patient registries are one of the most important sources of information in healthcare systems. Data linkage between Electronic Health Record Systems (EHRs) and registries, despite its challenges, is profitable. EHRs can be used for case finding in any patient registry to reduce the time and cost of case finding.

Investigating the advantages and disadvantages of electronic logbooks for education goals promotion in medical sciences students: A systematic review.

Background and Aims: Electronic logbook (E-Logbook) is one of the practical software in medical science that serves as an auxiliary tool for comprehensive education, formative evaluation, and student learning documentation in clinical education. E-logbooks are available to people on the Internet without any time or place restrictions. Experts' familiarity with e-logbooks and their advantages and disadvantages can be effective in their better design so professors and students can use their potential benefits. Therefore, this study examines the advantages and disadvantages of an e-logbook. Methods: This systematic review was conducted until June 13, 2022, by searching relevant keywords such as logbook, e-logbook, and medical students in PubMed, Scopus, and Web of Science databases. Data were extracted using the data extraction form. The contents of the studies were analyzed based on the study's aim. The results of the analyses were presented in the form of descriptive statistics (tables and figures). Results: Out of 365 retrieved studies, 13 were selected to investigate the advantages and disadvantages of e-logbooks. Most studies were conducted in Pakistan (n = 4) and focused on medical students with different specialties (n = 10). The advantages and disadvantages of e-logbooks were classified into nine and four categories, respectively. Most advantages of e-logbooks were related to monitoring and evaluating the performance of students and instructors (n = 11). Their most disadvantages were associated with hardware and software (n = 8). Conclusion: According to the results, e-logbooks can improve clinical education, provide feedback to people, control the achievement of educational goals, and increase professor-student interaction. Hence, it is recommended to address their disadvantages and barriers to improve the quality of students' performance.