Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

Data preprocessing moves the data from raw to ready for analysis. Data resulting from fraud compromises the quality of the data and the resulting analysis. It can exist in datasets such that it goes undetected since it is included in the analysis. This study proposed a process for measuring the effect of fraudulent data during data preparation and its possible influence on quality. The five-step process begins with identifying the business rules related to the business process(s) affected by fraud and their associated quality dimensions. This is followed by measuring the business rules in the specified timeframe, detecting fraudulent data, cleaning them, and measuring their quality after cleaning. The process was implemented in the case of occupational fraud within a hospital context and the illegal issuance of underserved sick leave. The aim of the application is to identify the quality dimensions that are influenced by the injected fraudulent data and how these dimensions are affected. This study agrees with the existing literature and confirms its effects on timeliness, coherence, believability, and interpretability. However, this did not show any effect on consistency. Further studies are needed to arrive at a generalizable list of the quality dimensions that fraud can affect.

Purpose: The increasing use of the terms “fake data” and “fake information” in digital systems has led to conceptual ambiguity and inconsistent usage across the literature. This study aims to clarify the definitional boundaries and relationships between these terms by examining how they are used and interpreted in existing research. Design/methodology/approach: A structured literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 334 studies published between 2009 and 2026 were selected after screening and eligibility assessment. Their abstracts were analyzed using thematic analysis, where recurring patterns were identified and encoded based on dimensions such as intent of creation, transformation processes and usage context. Findings: The results reveal that neither “fake data” nor “fake information” is explicitly defined in the literature, with meanings typically inferred from context. The analysis also shows a marked increase in the use of these terms since 2020, with fake data more prevalent in technical domains such as cybersecurity and data science, and fake information more common in media and social contexts. Research limitations/implications: The review is restricted to the computer science domain and its related fields (e.g. IT, data science and artificial intelligence), which may limit the generalizability of the findings to other disciplines. Despite these limitations, the study highlights the need for consistent terminology and provides a foundation for future research to refine and validate the proposed conceptual distinctions across broader contexts. Practical implications: The proposed framework supports improved classification, risk assessment and intervention strategies by distinguishing between data-level manipulation and information-level distortion in digital systems. Originality/value: This study introduces a data–information–knowledge-grounded, two-level framework that clarifies ambiguity in the use of fake data and fake information. The first level distinguishes between legitimate and deceptive intent, while the second differentiates based on what is being produced (data vs. information). This structure enables a clearer conceptual separation between fake data, fake information and synthetic data.