Artificial Intelligence in Pharmaceutical and Healthcare Research

Big Data and Cognitive Computing, Journal Year: 2023, Volume and Issue: 7(1), P. 10 - 10

Published: Jan. 11, 2023

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service emerging at greater pace. This review elaborates the opportunities challenges pharmaceutical research. literature was collected from domains such as PubMed, Science Direct Google scholar using specific keywords phrases ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. select articles published within last five years. application disease diagnosis, digital therapy, personalized treatment, drug discovery forecasting epidemics or pandemics extensively reviewed this article. Deep learning neural networks are most used technologies; Bayesian nonparametric models potential technologies for clinical trial design; natural language processing wearable devices patient identification monitoring. were applied predicting outbreak seasonal influenza, Zika, Ebola, Tuberculosis COVID-19. With advancement technologies, scientific community may witness rapid cost-effective well provide improved general public.

Language: Английский

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Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade

Pharmaceuticals, Journal Year: 2023, Volume and Issue: 16(2), P. 253 - 253

Published: Feb. 7, 2023

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs speed up development process of anti-cancer designs become urgent question for pharmaceutical industry. Computer-aided methods have played major role in cancer treatments over three decades. Recently, artificial intelligence emerged powerful promising technology faster, cheaper, more effective designs. This study is narrative review that reviews wide range applications intelligence-based design. We further clarify fundamental principles these methods, along with their advantages disadvantages. Furthermore, we collate large number databases, including omics database, epigenomics chemical compound databases. Other researchers can consider them adapt own requirements.

Language: Английский

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Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Cancers, Journal Year: 2023, Volume and Issue: 15(21), P. 5236 - 5236

Published: Oct. 31, 2023

Lung cancer remains one of the leading causes cancer-related deaths worldwide, emphasizing need for improved diagnostic and treatment approaches. In recent years, emergence artificial intelligence (AI) has sparked considerable interest in its potential role lung cancer. This review aims to provide an overview current state AI applications screening, diagnosis, treatment. algorithms like machine learning, deep radiomics have shown remarkable capabilities detection characterization nodules, thereby aiding accurate screening diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, even chest radiographs, accurately identifying suspicious nodules facilitating timely intervention. models exhibited promise utilizing biomarkers tumor markers supplementary tools, effectively enhancing specificity accuracy early detection. distinguish between benign malignant assisting radiologists making more informed decisions. Additionally, hold integrate multiple modalities clinical data, providing a comprehensive assessment. By high-quality including patient demographics, history, genetic profiles, predict responses guide selection optimal therapies. Notably, these success predicting likelihood response recurrence following targeted therapies optimizing radiation therapy patients. Implementing tools practice aid diagnosis management potentially improve outcomes, mortality morbidity

Language: Английский

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Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

BioMedInformatics, Journal Year: 2024, Volume and Issue: 4(1), P. 236 - 284

Published: Jan. 18, 2024

Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such cancer detection. This literature review synthesizes current research deep techniques applied to lung screening diagnosis. summarizes the state-of-the-art in detection, highlighting key advances, limitations, future directions. We prioritized studies utilizing major public datasets, LIDC, LUNA16, JSRT, provide comprehensive overview of field. focus architectures, including 2D 3D convolutional neural networks (CNNs), dual-path networks, Natural Language Processing (NLP) vision transformers (ViT). Across studies, models consistently outperformed traditional machine terms accuracy, sensitivity, specificity detection CT scans. is attributed ability automatically learn discriminative features from images model complex spatial relationships. However, several challenges remain be addressed before can widely deployed clinical practice. These include dependence training data, generalization across integration metadata, interpretability. Overall, demonstrates great potential precision medicine. more required rigorously validate address risks. provides insights both computer scientists clinicians, summarizing progress directions analysis.

Language: Английский

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Shaping the future of AI in healthcare through ethics and governance

Humanities and Social Sciences Communications, Journal Year: 2024, Volume and Issue: 11(1)

Published: March 15, 2024

Abstract The purpose of this research is to identify and evaluate the technical, ethical regulatory challenges related use Artificial Intelligence (AI) in healthcare. potential applications AI healthcare seem limitless vary their nature scope, ranging from privacy, research, informed consent, patient autonomy, accountability, health equity, fairness, AI-based diagnostic algorithms care management through automation for specific manual activities reduce paperwork human error. main faced by states regulating were identified, especially legal voids complexities adequate regulation better transparency. A few recommendations made protect data, mitigate risks regulate more efficiently international cooperation adoption harmonized standards under World Health Organization (WHO) line with its constitutional mandate digital public health. European Union (EU) law can serve as a model guidance WHO reform International Regulations (IHR).

Language: Английский

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Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

Diagnostics, Journal Year: 2022, Volume and Issue: 12(11), P. 2644 - 2644

Published: Oct. 31, 2022

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase lung management, from detection to assessment response treatment. The development imaging-based artificial intelligence (AI) models has potential play a key early customized treatment planning. Computer-aided nodules screening programs revolutionized disease. Moreover, possibility use AI approaches identify patients at risk developing during their life can help more targeted program. combination imaging features clinical laboratory data through giving promising results prediction patients’ outcomes, specific therapies, for toxic reaction development. In this review, we provide overview main AI-based tools imaging, including automated lesion detection, characterization, segmentation, outcome, radiologists clinicians foundation these applications scenario.

Language: Английский

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Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT

La radiologia medica, Journal Year: 2023, Volume and Issue: 129(1), P. 56 - 69

Published: Nov. 16, 2023

Abstract Objectives The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data classify lung nodules into benign or malignant categories, further different pathological subtypes Lung Imaging Reporting Data System (Lung-RADS) scores. Materials methods proposed was trained, validated, tested using three datasets: one public dataset, the Nodule Analysis 2016 (LUNA16) Grand challenge dataset ( n = 1004), two private datasets, Received Operation (LNOP) 1027) in Health Examination (LNHE) 1525). used stacked ensemble by employing machine (ML) approach with an AutoGluon-Tabular classifier. input variables were modified 3D convolutional neural network (CNN) features, radiomics features. Three classification tasks performed: Task 1: Classification of LUNA16 dataset; 2: subtypes; 3: Lung-RADS score. performance determined based on accuracy, recall, precision, F1-score. Ten-fold cross-validation applied each task. Results achieved high accuracy classifying categories LUNA 16 92.8%, as well F1-score 75.5% scores 80.4%. Conclusion Our provides accurate benign/malignant, subtypes, system.

Language: Английский

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Imaging Diagnosis of Hepatocellular Carcinoma: A State-of-the-Art Review

Diagnostics, Journal Year: 2023, Volume and Issue: 13(4), P. 625 - 625

Published: Feb. 8, 2023

Hepatocellular carcinoma (HCC) remains not only a cause of considerable part oncologic mortality, but also diagnostic and therapeutic challenge for healthcare systems worldwide. Early detection the disease consequential adequate therapy are imperative to increase patients’ quality life survival. Imaging plays, therefore, crucial role in surveillance patients at risk, diagnosis HCC nodules, as well follow-up post-treatment. The unique imaging characteristics lesions, deriving mainly from assessment their vascularity on contrast-enhanced computed tomography (CT), magnetic resonance (MR) or ultrasound (CEUS), allow more accurate, noninvasive staging. management has further expanded beyond plain confirmation suspected due introduction hepatobiliary MRI contrast agents, which hepatocarcinogenesis even an early stage. Moreover, recent technological advancements artificial intelligence (AI) radiology contribute important tool prediction, prognosis evaluation treatment response clinical course disease. This review presents current modalities central risk with HCC.

Language: Английский

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Unveiling the potential of proteomic and genetic signatures for precision therapeutics in lung cancer management

Cellular Signalling, Journal Year: 2023, Volume and Issue: 113, P. 110932 - 110932

Published: Oct. 21, 2023

Language: Английский

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Recent advances in lung cancer research: unravelling the future of treatment

Updates in Surgery, Journal Year: 2024, Volume and Issue: 76(6), P. 2129 - 2140

Published: April 6, 2024

Language: Английский

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