Beyond performance metrics: evaluating the unique value of generative AI in hybrid cybersecurity threat detection.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Semantic equivalence verification of source code is essential for software quality assurance, but remains underexplored for High Performance Computing (HPC) codes, especially Fortran. This paper systematically evaluates large language models (LLMs) for semantic equivalence verification of HPC codes and proposes methods to improve performance. We construct a new dataset of 15,000 code pairs in Fortran, C, C++, and Python from AtCoder submissions, covering semantically equivalent and non-equivalent pairs under easy and difficult settings. Prompt-based experiments show that LLMs achieve comparable performance on Fortran and general-purpose languages, but their accuracy drops substantially for the difficult task settings. To address this, we propose a feature-extraction-based method that uses LLM hidden-layer representations as semantic encoders for conventional machine learning (ML) classifiers, achieving an F1 score of up to 0.913 on difficult Fortran pairs. Furthermore, majority-vote ensembles over multiple LLMs and ML classifiers improve robustness and reach an F1 score of 0.972. These results demonstrate that leveraging LLM latent representations and simple ensembles significantly enhances semantic equivalence verification for HPC codes, providing practical guidance for refactoring support, regression testing, and bug detection in scientific software.

BackgroundAdverse events refer to incidents with potential or actual harm to patients in hospitals. These events are typically documented through patient safety event (PSE) reports, which consist of detailed narratives providing contextual information on the occurrences. Accurate classification of PSE reports is crucial for patient safety monitoring. However, this process faces challenges due to inconsistencies in classifications and the sheer volume of reports. Recent advancements in text representation, particularly contextual text representation derived from transformer-based language models, offer a promising solution for more precise PSE report classification. Integrating the machine learning (ML) classifier necessitates a balance between human expertise and artificial intelligence (AI). Central to this integration is the concept of explainability, which is crucial for building trust and ensuring effective human-AI collaboration.ObjectiveThis study aims to investigate the efficacy of ML classifiers trained using contextual text representation in automatically classifying PSE reports. Furthermore, the study presents an interface that integrates the ML classifier with the explainability technique to facilitate human-AI collaboration for PSE report classification.MethodsThis study used a data set of 861 PSE reports from a large academic hospital’s maternity units in the Southeastern United States. Various ML classifiers were trained with both static and contextual text representations of PSE reports. The trained ML classifiers were evaluated with multiclass classification metrics and the confusion matrix. The local interpretable model-agnostic explanations (LIME) technique was used to provide the rationale for the ML classifier’s predictions. An interface that integrates the ML classifier with the LIME technique was designed for incident reporting systems.ResultsThe top-performing classifier using contextual representation was able to obtain an accuracy of 75.4% (95/126) compared to an accuracy of 66.7% (84/126) by the top-performing classifier trained using static text representation. A PSE reporting interface has been designed to facilitate human-AI collaboration in PSE report classification. In this design, the ML classifier recommends the top 2 most probable event types, along with the explanations for the prediction, enabling PSE reporters and patient safety analysts to choose the most suitable one. The LIME technique showed that the classifier occasionally relies on arbitrary words for classification, emphasizing the necessity of human oversight.ConclusionsThis study demonstrates that training ML classifiers with contextual text representations can significantly enhance the accuracy of PSE report classification. The interface designed in this study lays the foundation for human-AI collaboration in the classification of PSE reports. The insights gained from this research enhance the decision-making process in PSE report classification, enabling hospitals to more efficiently identify potential risks and hazards and enabling patient safety analysts to take timely actions to prevent patient harm.

In recent years, peptides have grabbed significant attention across many fields, including pharmaceutical, biomedical, and biotechnological industries, owing to their notable biological activity, low toxicity, and high specificity. Naturally occurring peptides play crucial roles in handling different biological processes (cellular signaling, immune responses, and enzymatic functions, etc.), while laboratory-made synthetic peptides can be adopted for many applied industrial applications. Following the practical problems in large-scale peptide synthesis in the laboratory which demand high cost and time, recent developments have benefited from the best use of machine learning (ML), deep learning, active learning, reinforcement learning (RL), generative artificial intelligence (AI), and large language models (LLMs) to reduce the number of experiments. ML algorithms enable the prediction of the peptide structure-activity relationship, bioavailability, and other drug-like properties with high accuracy. The integration of AI with peptide-based therapeutics design marks a paradigm shift in drug discovery which was otherwise dominated by small organic molecules. This review comprehensively examines AI-driven methodologies, including classical ML approaches, deep generative models, RL, and LLMs, that overcome historical limitations in peptide design, such as structural flexibility, enzymatic degradation, and membrane impermeability. Recent advances in structure-aware algorithms and sequence-based frameworks have accelerated peptide-based therapeutic development across oncology, metabolic disorders, and infectious diseases. Despite challenges in data scarcity and validation gaps, the convergence of computational prediction with experimental automation promises clinical translation of AI-designed peptides in the near future. This review highlights the transformative potential of AI in ushering a new era of precision peptide therapeutics.

The recent performance leap of Large Language Models (LLMs) opens up new opportunities across numerous industrial applications and domains. However, the potential erroneous behavior (e.g., the generation of misinformation and hallucination) has also raised severe concerns for the trustworthiness of LLMs, especially in safety-, security- and reliability-sensitive industrial scenarios, potentially hindering real-world adoptions. While uncertainty estimation has shown its potential for interpreting the prediction risks made by classic machine learning (ML) models, the unique characteristics of recent LLMs (e.g., adopting self-attention mechanism as its core, very largescale model size, often used in generative contexts) pose new challenges for the behavior analysis of LLMs. Up to the present, little progress has been made to better understand whether and to what extent uncertainty estimation can help characterize the capability boundary of an LLM, to counteract its undesired behavior, which is considered to be of great importance with the potential wide-range applications of LLMs across industry domains. To bridge the gap, in this paper, we initiate an early exploratory study of the risk assessment of LLMs from the lens of uncertainty. In particular, we conduct a large-scale study with as many as twelve uncertainty estimation methods and eight general LLMs on four NLP tasks and seven programming-capable LLMs on two code generation tasks to investigate to what extent uncertainty estimation techniques could help characterize the prediction risks of LLMs. Our findings confirm the potential of uncertainty estimation for revealing LLMs’ uncertain/nonfactual predictions. The insights derived from our study can pave the way for more advanced analysis and research on LLMs, ultimately aiming at enhancing their trustworthiness.

In the case of chronic management diseases like diabetes, cardiovascular disorders, and cancer, there is a significant challenge of predicting the reaction of patients to medical treatment. Patient health status, medical history, genetic and treatment arrangements create difficulty in the health practitioners determining the most effective treatment to be offered to each individual. The latest developments in Artificial Intelligence (AI) can open new possibilities in terms of using the large amounts of medical data and helping in the clinical decision-making. The proposed project aims at the creation of a smart system where Generative Artificial Intelligence and Large Language Models (LLMs) are applied to forecast patient treatment reactions and help in customized care. The suggested system will challenge structured and unstructured healthcare data, such as establishment health records, lab reports, treatment history, and clinical notes. Identifying treatment outcome patterns based on patient characteristics is done using machine learning and deep learning techniques. Large Language Models are also integrated to analyze and comprehend written medical data including doctor notes and patient reports so that clinical data could be interpreted more accurately. Python is used to implement the system along with deep learning systems such as PyTorch or TensorFlow, and healthcare datasets made publicly available are used in training and evaluation. Some of the standard metrics that are used to evaluate the performance of the system are accuracy, precision, recall, and F1-score in order to determine the effectiveness of prediction. The last system gives insights on whether a patient is likely to respond a given treatment in a positive way to assist medical practitioners give more effective and personalized treatment. The proposed system helps to reveal the potential of intelligent healthcare technologies by allowing them to enhance the planning of the treatment and supporting the personalized medicine through the combination of Generative AI, predictive analytics, and natural language processing.

Background: Screening and early detection play a key role in cervical cancer prevention. The present study predicts the outcome of various diagnostic tests used to diagnose cervical cancer using machine learning algorithms. Methods: The present study ran various cervical cancer risk factors on a machine learning (ML) classifier to predict outcomes of Hinselmann, Schiller, cytology and biopsy. The dataset is publicly available on the Machine Learning Repository website of the University of California Irvine. The imbalanced dataset was pre-processed using oversampling methods. The significantly varied features between the two levels of a response variable were used to train the machine learning classifiers on MATLAB. The classifiers used were Decision Trees, Support Vector Machine, K-Nearest Neighbours and Ensemble learning classifiers. The performance metrics of the classifiers were expressed as accuracy, the area under the receiver operator characteristic (AU-ROC) curve, sensitivity and specificity. Results: The Fine Gaussian SVM classifier was the best to classify Hinselmann, cytology and biopsy with the accuracy of 97.5 %, 62.5 % and 98 %, respectively. However, Boosted trees performed best in the classification of Schiller with 81.3 % accuracy. Conclusion: The present study selected optimised features among multiple risk factors to train various ML classifiers to predict cervical cancer.

Bronchiolitis remains a frequent reason for hospitalization in infants during the winter season. Epidemiologic surveillance remains crucial in the era of widespread immunoprophylaxis for the leading viral agent causing bronchiolitis. We investigated the performance of classical machine learning (ML) models, Deep Learning (DL), and a pre-trained large language model (LLM) in classifying bronchiolitis diagnosis from the free-text-diagnosis field of the emergency department electronic health records (EHRs). As a secondary aim, we evaluated the diagnostic accuracy of the actual official administrative ICD-9 encoding for Bronchiolitis diagnosis. 28,557 records of infants < 1 year with complete discharge diagnoses fields were retrieved between the years 2007–2018 and manually classified by an expert pediatrician to create the gold standard diagnosis set for training the algorithm. After data pre-processing, classical ML models (Random Forest, Decision Tree, Gradient Boosting Machine, Linear Discriminant Analysis, Support Vector Machine), a Deep Learning (DL) tool, and a pre-trained LLM (GPT-5) were evaluated using balanced accuracy, sensitivity, and F1 scores. The official administrative ICD-9 encoding classification accuracy was compared to the gold standard. Overall, 1,903 of 28,557 records (6.7%) were classified as bronchiolitis by the gold standard approach. The DL model and GPT-5 outperformed traditional ML models, achieving higher sensitivities (0.97, 95%CI 0.96-1.00, and 0.98, 95% CI 0.98–0.99, respectively), F1 scores (0.96, 95% CI 0.95–0.99, and 0.99, 95% CI 0.98–0.99, respectively), and balanced accuracy (0.98, 95%CI 0.98-1.00, and 0.99, 95% CI 0.99–0.99, respectively). Traditional ML models showed sensitivities between 0.77 and 0.98, F1 scores between 0.86 and 0.96, and balanced accuracies between 0.88 and 0.96. ICD-9 codes showed sensitivity of 85.9% (95% CI 84.27–87.45), and specificity of 98.5% (95% CI 98.36–98.65). To our knowledge, this is the first study directly comparing an LLM, deep learning, and multiple classical ML models for bronchiolitis surveillance in the post-Nirsevimab era. DL and GPT-5 outperformed traditional ML-based tools in identifying bronchiolitis diagnoses and ICD-9 diagnosis coding. AI-based tools hold significant potential for improving epidemiologic surveillance of bronchiolitis from emergency department EHRs. Not applicable.

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimensional tabular data from 9,134 patients across four hospitals. Seven CML models, including XGBoost and random forest (RF), were evaluated alongside eight LLMs, such as GPT-4 and Mistral-7b, which performed zero-shot classification on text-converted structured data. Additionally, Mistral-7b was fine-tuned using the QLoRA approach. XGBoost and RF demonstrated superior performance among CMLs, achieving F1 scores of 0.87 and 0.83 for internal and external validation, respectively. GPT-4 led the LLM category with an F1 score of 0.43, while fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, yielding a stable F1 score of 0.74 during external validation. Although LLMs showed moderate performance in zero-shot classification, fine-tuning substantially enhanced their effectiveness, potentially bridging the gap with CML models. However, CMLs still outperformed LLMs in handling high-dimensional tabular data tasks. This study highlights the potential of both CMLs and fine-tuned LLMs in medical predictive modeling, while emphasizing the current superiority of CMLs for structured data analysis.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-26705-7.

Structural health monitoring is a popular inspection method that utilizes acoustic emission (AE) signals for fault detection in engineering infrastructures. Diagnosis based on the propagation of AE signals along any surface material offers an attractive solution for fault identification. However, the classification of AE signals originating from failure events, especially coating failure (coating disbondment), is a challenging task given the AE signature of each material. Thus, different experimental settings and analyses of AE signals are required to classify the various types of coating failures, and they are time-consuming and expensive. Hence, to address these issues, we utilized machine learning (ML) classification models in this work to evaluate epoxy-based-protective-coating disbondment based on the AE principle. A coating disbondment experiment consisting of coated carbon steel test panels for the collection of AE signals was implemented. The obtained AE signals were then processed to construct the final dataset to train various state-of-the-art ML classification models to divide the failure severity of coating disbondment into three classes. Consequently, methods for the extraction of useful features, the handling of data imbalance, and a reduction in the bias of ML models were also effectively utilized in this study. Evaluations of state-of-the-art ML classification models on the AE signal dataset in terms of standard metrics revealed that the decision forest classification model outperformed the other state-of-the-art models, with accuracy, precision, recall, and F1 score values of 99.48%, 98.76%, 97.58%, and 98.17%, respectively. These results demonstrate the effectiveness of utilizing ML classification models for the failure severity prediction of protective-coating defects via AE signals.

Deep phenotyping is the detailed description of patient signs and symptoms using concepts from an ontology. The deep phenotyping of the numerous physician notes in electronic health records requires high throughput methods. Over the past 30 years, progress toward making high-throughput phenotyping feasible. In this study, we demonstrate that a large language model and a hybrid NLP model (combining word vectors with a machine learning classifier) can perform high throughput phenotyping on physician notes with high accuracy. Large language models will likely emerge as the preferred method for high throughput deep phenotyping physician notes.Clinical relevance: Large language models will likely emerge as the dominant method for the high throughput phenotyping of signs and symptoms in physician notes.

This study aimed to investigate the role of machine learning (ML) classifiers to determine the most informative multiparametric (mp) magnetic resonance imaging (MRI) features in predicting the treatment outcome of high-intensity focused ultrasound (HIFU) ablation with an immediate nonperfused volume (NPV) ratio of at least 90%. Seventy-three women who underwent HIFU treatment were divided into groups A (n=47) and B (n=26), comprising patients with an NPV ratio of at least 90% and <90%, respectively. An ensemble feature ranking model was introduced based on the score values assigned to the features by five different ML classifiers to determine the most informative mpMRI features. The relationship between the mpMRI features and the immediate NPV ratio of 90% was evaluated using Pearson's correlation coefficients. The diagnostic ability of the ML classifiers was evaluated using standard performance metrics, including the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity in eight folds cross-validation. For all the 12 most informative features, the area under receiver operating characteristic curve (AUROC), accuracy, specificity, and sensitivity ranged from 0.5 to 0.97, 0.34 to 0.97, 0.56 to 1.0, and 0.87 to 1.0, respectively. The gradient boosting (GBM) classifier demonstrated the best predictive performance with an AUROC of 0.95 and accuracy of 0.92, followed by the random forest, AdaBoost, logistic regression, and support vector classifiers, which yielded an AUROC of 0.92, 0.92, 0.83, and 0.78 and accuracy of 0.96, 0.88, 0.84, and 0.84, respectively. GBM had the best classifier performance with the best performing features from each mpMRI group, Ktrans ratio of the fibroid to the myometrium, the ratio of area under the curve of the fibroid to the myometrium, subcutaneous fat thickness, the ratio of apparent diffusion coefficient value of fibroid to the myometrium, and T2-signal intensity of the fibroid. The preliminary findings of this study suggest that the most informative and best performing features from each mpMRI group should be considered for predicting the treatment outcome of HIFU ablation to achieve an immediate NPV ratio of 90%.

Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored. To assess the performance of ML in classifying glioma tumor grades based on various WHO criteria. Retrospective. A neuropathologist regraded gliomas of 237 patients into WHO 2016 and 2021 from 2007 criteria. Multicentric 0.5 to 3 Tesla; pre- and post-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery. Radiomic features were selected using random forest-recursive feature elimination. The synthetic minority over-sampling technique (SMOTE) was implemented for data augmentation. Stratified 10-fold cross-validation with and without SMOTE was used to evaluate 11 classifiers for 3-grade (2, 3, and 4; WHO 2016 and 2021) and 2-grade (low and high grade; WHO 2007 and 2021) classification. Additionally, we developed the models on data randomly divided into training and test sets (mixed-data analysis), or data divided based on the centers (independent-data analysis). We assessed ML classifiers using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). Top performances were compared with a t-test and categorical data with the chi-square test using a significance level of P < 0.05. In the mixed-data analysis, Stacking Classifier without SMOTE achieved the highest accuracy (0.86) and AUC (0.92) in 3-grade WHO 2021 grouping. The results of WHO 2021 were significantly better than WHO 2016 (P-value<0.0001). In the 2-grade analysis, ML achieved 1.00 in all metrics. In the independent-data analysis, ML classifiers showed strong discrimination between grade 2 and 4, despite lower performance metrics than the mixed analysis. ML algorithms performed better in glioma tumor grading based on WHO 2021 criteria. Nonetheless, the clinical use of ML classifiers needs further investigation. 3 TECHNICAL EFFICACY: Stage 2.

ABSTRACT: The synergistic evolution of Artificial Intelligence (AI) and Data Science represents one of the most transformative technological developments of the 21st century. This paper provides a comprehensive survey and analysis of contemporary advances across this integrated field, moving beyond siloed examinations to explore their convergence. We trace the foundational journey from classical machine learning (ML) to the deep learning revolution and onward to today's frontier of generative AI, large language models (LLMs), and neuro-symbolic systems. Crucially, we position these algorithmic breakthroughs within the enabling ecosystem of modern data science, emphasizing the critical role of advances in data engineering (e.g., vector databases, data lakes), computational frameworks (e.g., distributed computing, specialized hardware like TPUs/GPUs), and methodological rigor (e.g., MLOps, explainable AI - XAI). A novel framework, the "AI-Data Science Flywheel," is introduced to conceptualize how scalable data infrastructure fuels more sophisticated AI models, whose outputs in turn generate new, high-value data, creating a virtuous cycle of innovation. Our methodology combines a systematic literature review of over 200 key publications from 2018-2024 with a quantitative meta-analysis of performance benchmarks across 10 standard datasets (e.g., ImageNet, GLUE, Open LLM Leaderboard) and a qualitative case study analysis of three industry verticals: healthcare (AI-driven drug discovery), finance (algorithmic risk assessment), and climate science (climate informatics). Results indicate that while model capabilities on narrow tasks have seen near-superhuman performance gains (e.g., 99.5% accuracy on ImageNet classification), significant challenges persist in areas of generalization, energy efficiency, bias mitigation, and the integration of causal reasoning. The case studies reveal a common pattern: successful deployment hinges less on model novelty and more on robust data pipelines and effective human-AI collaboration frameworks. This paper concludes that the future trajectory of AI and Data Science will be defined by a shift from "bigger models" to "smarter, more efficient, and more responsible systems." Key research vectors include the pursuit of artificial general intelligence (AGI) pathways, federated learning for privacy preservation, quantum machine learning, and the development of comprehensive ethical and regulatory frameworks to ensure these powerful technologies yield broad societal benefit.