Revolutionizing healthcare: the role of artificial intelligence in clinical practice

BMC Medical Education, Journal Year: 2023, Volume and Issue: 23(1)

Published: Sept. 22, 2023

Abstract Introduction Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in practice is crucial successful implementation equipping providers essential knowledge tools. Research Significance This review article provides a comprehensive up-to-date overview current state practice, its applications disease diagnosis, treatment recommendations, engagement. It also discusses associated challenges, covering ethical legal considerations need human expertise. By doing so, enhances understanding significance supports organizations effectively adopting technologies. Materials Methods The investigation analyzed use system relevant indexed literature, such as PubMed/Medline, Scopus, EMBASE, no time constraints limited articles published English. focused question explores impact applying settings outcomes this application. Results Integrating holds excellent improving selection, laboratory testing. tools leverage large datasets identify patterns surpass performance several aspects. offers increased accuracy, reduced costs, savings while minimizing errors. personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual assistants, support mental care, education, influence patient-physician trust. Conclusion be used diagnose diseases, develop plans, assist clinicians decision-making. Rather than simply automating tasks, about developing technologies that across settings. However, challenges related data privacy, bias, expertise must addressed responsible effective healthcare.

Language: Английский

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AI's role in revolutionizing personalized medicine by reshaping pharmacogenomics and drug therapy

Intelligent Pharmacy, Journal Year: 2024, Volume and Issue: 2(5), P. 643 - 650

Published: Aug. 20, 2024

Language: Английский

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Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress

Cancer Discovery, Journal Year: 2024, Volume and Issue: 14(3), P. 508 - 523

Published: Jan. 18, 2024

Rapid proliferation is a hallmark of cancer associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways are incompletely understood. Here, we develop an ensemble predictive models elucidate how mutations impact the response common RS-inducing (RSi) agents. The implement recent advances in deep learning facilitate multidrug prediction and mechanistic interpretation. Initial studies tumor cells identify 41 assemblies integrate alterations hundreds genes for accurate prediction. These cover roles transcription, repair, cell-cycle checkpoints, growth signaling, which 30 shown by loss-of-function genetic screens regulate or restart. model translates cisplatin-treated cervical patients, highlighting RTK-JAK-STAT assembly governing resistance. This study defines compendium mechanisms affect therapeutic responses, implications precision medicine.

Language: Английский

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Polydopamine-Coated Zn-MOF-74 Nanocarriers: Versatile Drug Delivery Systems with Enhanced Biocompatibility and Cancer Therapeutic Efficacy

Journal of Inorganic and Organometallic Polymers and Materials, Journal Year: 2024, Volume and Issue: unknown

Published: July 13, 2024

Language: Английский

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Drug discovery and mechanism prediction with explainable graph neural networks

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 2, 2025

Language: Английский

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Improving drug response prediction via integrating gene relationships with deep learning

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(3)

Published: March 21, 2024

Predicting the drug response of cancer cell lines is crucial for advancing personalized treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques integrate multiple valuable information, including gene interaction relationships, expression profiles molecular topologies, enhance prediction accuracy robustness. We demonstrated superior performance DIPK compared existing methods on both known novel cells drugs, underscoring importance relationships in prediction. addition, extends its applicability single-cell RNA sequencing data, showcasing capability single-cell-level identification. Further, assess clinical data. accurately predicted higher paclitaxel pathological complete (pCR) group residual disease group, affirming better pCR chemotherapy compound. believe that integration into decision-making processes has potential individualized treatment strategies patients.

Language: Английский

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Overcoming limitations in current measures of drug response may enable AI-driven precision oncology

npj Precision Oncology, Journal Year: 2024, Volume and Issue: 8(1)

Published: April 24, 2024

Abstract Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation standard measures that hinders the development personalized – they focus on absolute effects but do not capture relative differences between cancer subtypes. Our work suggests using z-scored response mitigates these limitations and leads to meaningful predictions, opening door for sophisticated ML oncology models.

Language: Английский

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Conformal prediction of molecule-induced cancer cell growth inhibition challenged by strong distribution shifts

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 17, 2024

Abstract The drug discovery process often employs phenotypic and target-based virtual screening to identify potential candidates. Despite the longstanding dominance of approaches, is undergoing a resurgence due its being now better understood. In context cancer cell lines, well-established experimental system for screens, molecules are tested their whole-cell activity, as summarized by half-maximal inhibitory concentrations. Machine learning has emerged potent tool computationally guiding such yet important research gaps persist. Consequently, this study focuses on application Conformal Prediction (CP) predict activities novel specific lines. Two CP models were constructed evaluated each line, resulting in total 120 performance evaluations (60 lines x 2 models) per training-test partition. From comprehensive evaluation, we concluded that, regardless line or model, with smaller CP-calculated confidence intervals tend have predicted errors once measured revealed. It was also possible anticipate dissimilar test across 50 more These outcomes demonstrate robust efficacy that can achieve realistic challenging scenarios, thereby providing valuable insights enhancing decision-making processes discovery.

Language: Английский

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The Hallmarks of Predictive Oncology

Cancer Discovery, Journal Year: 2025, Volume and Issue: 15(2), P. 271 - 285

Published: Jan. 6, 2025

Abstract The rapid evolution of machine learning has led to a proliferation sophisticated models for predicting therapeutic responses in cancer. While many these show promise research, standards clinical evaluation and adoption are lacking. Here, we propose seven hallmarks by which predictive oncology can be assessed compared. These Data Relevance Actionability, Expressive Architecture, Standardized Benchmarking, Generalizability, Interpretability, Accessibility Reproducibility, Fairness. Considerations each hallmark discussed along with an example model scorecard. We encourage the broader community, including researchers, clinicians, regulators, engage shaping guidelines toward concise set standards. Significance: As field artificial intelligence evolves rapidly, intended capture fundamental, complementary concepts necessary progress timely modeling precision oncology. Through hallmarks, hope establish that enable symbiotic development

Language: Английский

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MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction

BMC Bioinformatics, Journal Year: 2025, Volume and Issue: 26(1)

Published: Jan. 17, 2025

Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional methods relying on demographic genetic data often fall short accuracy robustness. Recent graph-based models, while promising, frequently neglect role of atomic interactions fail integrate drug fingerprints with SMILES comprehensive molecular graph construction. We introduce multimodal multi-channel attention network adaptive fusion (MGATAF), a framework designed enhance predictions by capturing both local global among nodes. MGATAF improves representation integrating fingerprints, resulting more precise effects. The methodology involves constructing graphs, employing networks capture diverse interactions, using these at multiple abstraction levels. Empirical results demonstrate MGATAF's superior performance compared traditional other techniques. For example, GDSC dataset, achieved 5.12% improvement Pearson correlation coefficient (PCC), reaching 0.9312 an RMSE 0.0225. Similarly, new cell-line tests, outperformed baselines PCC 0.8536 0.0321 0.7364 0.0531 CCLE dataset. significantly advances effectively types complex interactions. This enhances offers robust tool personalized medicine, potentially leading safer Future research can expand this work exploring additional modalities refining mechanisms.

Language: Английский

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