Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

Опубликована: Ноя. 16, 2023

<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent contextually fitting responses. models are type artificial intelligence (AI) that have emerged powerful tools for wide range tasks, including natural processing (NLP), machine translation, question-answering. This survey paper provides comprehensive overview LLMs, their history, architecture, training methods, applications, challenges. The begins by discussing fundamental concepts generative AI architecture pre- trained transformers (GPT). It then an history evolution over time, different methods been used train them. discusses applications medical, education, finance, engineering. also how LLMs shaping future they can be solve real-world problems. challenges associated with deploying scenarios, ethical considerations, model biases, interpretability, computational resource requirements. highlights techniques enhancing robustness controllability addressing bias, fairness, generation quality issues. Finally, concludes highlighting LLM research need addressed order make more reliable useful. is intended provide researchers, practitioners, enthusiasts understanding evolution, By consolidating state-of-the-art knowledge field, this serves valuable further advancements development utilization applications. GitHub repo project available at https://github.com/anas-zafar/LLM-Survey</p>

Язык: Английский

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From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare

Current Research in Biotechnology, Год журнала: 2023, Номер 7, С. 100164 - 100164

Опубликована: Ноя. 22, 2023

The medicine and healthcare sector has been evolving advancing very fast. advancement initiated shaped by the applications of data-driven, robust, efficient machine learning (ML) to deep (DL) technologies. ML in medical is developing quickly, causing rapid progress, reshaping medicine, improving clinician patient experiences. technologies evolved into data-hungry DL approaches, which are more robust dealing with data. This article reviews some critical data-driven aspects intelligence field. In this direction, illustrated recent progress science using two categories: firstly, development data uses and, secondly, Chabot particularly on ChatGPT. Here, we discuss ML, DL, transition requirements from DL. To science, illustrate prospective studies image data, newly interpretation EMR or EHR, big personalized dataset shifts artificial (AI). Simultaneously, recently developed DL-enabled ChatGPT technology. Finally, summarize broad role significant challenges for implementing healthcare. overview paradigm shift will benefit researchers immensely.

Язык: Английский

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A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)

IEEE Access, Год журнала: 2024, Номер 12, С. 57815 - 57836

Опубликована: Янв. 1, 2024

YOLO (You Only Look Once) is an extensively utilized object detection algorithm that has found applications in various medical tasks. This been accompanied by the emergence of numerous novel variants recent years, such as YOLOv7 and YOLOv8. study encompasses a systematic exploration PubMed database to identify peer-reviewed articles published between 2018 2023. The search procedure 124 relevant studies employed for diverse tasks including lesion detection, skin classification, retinal abnormality identification, cardiac brain tumor segmentation, personal protective equipment detection. findings demonstrated effectiveness outperforming alternative existing methods these However, review also unveiled certain limitations, well-balanced annotated datasets, high computational demands. To conclude, highlights identified research gaps proposes future directions leveraging potential

Язык: Английский

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AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring

Biosensors, Год журнала: 2024, Номер 14(7), С. 356 - 356

Опубликована: Июль 22, 2024

The steady progress in consumer electronics, together with improvement microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, some them are applied point-of-care (PoC) tests as a reliable source evaluation patient's condition. Current practices still based on laboratory tests, preceded by collection biological samples, then tested clinical conditions trained personnel specialistic equipment. In practice, collecting passive/active physiological behavioral from patients real time feeding artificial intelligence (AI) models can significantly improve decision process regarding diagnosis treatment procedures via omission conventional sampling while excluding pathologists. A combination novel methods digital traditional biomarker detection portable, autonomous, miniaturized revolutionize medical diagnostics coming years. This article focuses comparison modern techniques AI machine learning (ML). presented technologies will bypass laboratories start being commercialized, should lead or substitution current Their application PoC settings technology accessible every patient appears be possibility. Research this field is expected intensify Technological advancements sensors biosensors anticipated enable continuous real-time analysis various omics fields, fostering early disease intervention strategies. integration health platforms would predictive personalized healthcare, emphasizing importance interdisciplinary collaboration related scientific fields.

Язык: Английский

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Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

ACS Sensors, Год журнала: 2024, Номер 9(9), С. 4495 - 4519

Опубликована: Авг. 15, 2024

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration Machine Learning (ML) into biosensors ushered in a new era innovation the field PoCT. This article investigates numerous uses transformational possibilities ML improving for algorithms, which are capable processing interpreting complicated biological data, have transformed accuracy, sensitivity, speed procedures variety healthcare contexts. review explores multifaceted applications models, including classification regression, displaying how they contribute to capabilities biosensors. roles ML-assisted electrochemical sensors, lab-on-a-chip electrochemiluminescence/chemiluminescence colorimetric wearable sensors diagnosis explained detail. Given increasingly important role PoCT, this study serves valuable reference researchers, clinicians, policymakers interested understanding emerging landscape point-of-care diagnostics.

Язык: Английский

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Review—Quantum Biosensors: Principles and Applications in Medical Diagnostics

ECS Sensors Plus, Год журнала: 2024, Номер 3(2), С. 025001 - 025001

Опубликована: Май 7, 2024

Originating at the intersection of physics and biosensing, quantum biosensors (QB) are transforming medical diagnostics personalized medicine by exploiting phenomena to amplify sensitivity, specificity, detection speed compared traditional biosensors. Their foundation lies in fusion biological entities like DNA, proteins, or enzymes with sensors, which elicits discernible alterations light emissions when interacting sample molecules. prowess identifying disease-linked biomarkers presents an avenue for early diagnoses conditions Alzheimer’s cancer. Beyond this, they enable real-time monitoring treatment responses capturing dynamism biomarkers, but QB still faces challenges, such as issues stability, reproducibility, intricate interactions. Moreover, seamless integration into prevailing diagnostic frameworks necessitates careful consideration. Looking ahead, evolution navigates uncharted territories. Innovations fabrication techniques, interdisciplinary collaborations, standardization protocols emerge pivotal areas exploration. This comprehensive discourse encapsulates QB’s principles, diverse iterations, burgeoning utilities. It delves inherent challenges limitations, shedding on potential trajectories future research. As continues evolve, its redefine becomes increasingly tangible. The saga resonates possibilities, poised reshape landscape profoundly.

Язык: Английский

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AI-Driven Deep Learning Techniques in Protein Structure Prediction

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(15), С. 8426 - 8426

Опубликована: Авг. 1, 2024

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive of the computational models used in predicting protein structure. It covers progression from established modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with brief introduction structures, modeling, AI. section on discuss homology ab initio threading. next deep learning-based models. introduces some AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. also discusses how techniques have been integrated into frameworks like Swiss-Model, Rosetta, I-TASSER. model performance compared using rankings CASP14 (Critical Assessment Structure Prediction) CASP15. CASP16 ongoing, its results are not included this review. Continuous Automated Model EvaluatiOn (CAMEO) complements biennial CASP experiment. Template score (TM-score), global distance test total (GDT_TS), Local Distance Difference Test (lDDT) discussed too. then acknowledges ongoing difficulties emphasizes necessity additional searches dynamic behavior, conformational changes, protein-protein interactions. In application section, applications various fields drug design, industry, education, novel development. summary, provides overview latest advancements predictions. significant achieved by identifies potential areas further investigation.

Язык: Английский

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Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 6, 2025

Язык: Английский

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Recent advancements in machine learning enabled portable and wearable biosensors

Talanta Open, Год журнала: 2023, Номер 8, С. 100267 - 100267

Опубликована: Окт. 30, 2023

Recent advances in noninvasive portable and wearable biosensors have attracted significant attention due to their capability offer continual physiological information for continuous healthcare monitoring through the collection of biological signals. To make collected data understandable improve efficacy these biosensors, scientists integrated machine learning (ML) with analyze large sensing various ML algorithms. In this article, we highlighted recent developments ML-enabled biosensors. Initially, introduced discussed basic features algorithms used processing build an intelligent biosensor system clinical decisions. Next, principles application different models diverse applications, impact on performance are discussed. The last section highlights challenges (such as privacy, consistency, stability, accuracy, scalable production, adaptive capacity), future prospects, necessary steps required address issues, spotlighting revolutionizing industry development next-generation efficient

Язык: Английский

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Artificial Intelligence−Powered Electrochemical Sensor: Recent Advances, Challenges, and Prospects

Heliyon, Год журнала: 2024, Номер 10(18), С. e37964 - e37964

Опубликована: Сен. 1, 2024

Язык: Английский

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