Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 73 - 84
Опубликована: Окт. 8, 2024
Язык: Английский
Biomolecules, Год журнала: 2024, Номер 14(8), С. 911 - 911
Опубликована: Июль 26, 2024
A highly critical event in a virus's life cycle is successfully entering given host. This process begins when viral glycoprotein interacts with target cell receptor, which provides the molecular basis for virus-host interactions novel drug discovery. Over years, extensive research has been carried out field of interaction, generating massive number genetic and data sources. These datasets are an asset predicting at level using machine learning (ML), subset artificial intelligence (AI). In this direction, ML tools now being applied to recognize patterns these predict between virus host cells protein-protein protein-sugar levels, as well perform transcriptional translational analysis. On other end, deep (DL) algorithms-a subfield ML-can extract high-level features from very large hidden within genomic sequences images develop models rapid discovery predictions that address pathogenic viruses displaying heightened affinity receptor docking enhanced entry. DL pivotal forces, driving innovation their ability analysis enormous efficient, cost-effective, accurate, high-throughput manner. review focuses on complexity light current advances AI pathogenesis improve new treatments prevention strategies.
Язык: Английский
Процитировано
4
Current Infectious Disease Reports, Год журнала: 2025, Номер 27(1)
Опубликована: Фев. 13, 2025
Abstract Purpose of Review The goal this paper is to explore the impact digital health technologies (DHTs) on antibiotic utilization in older adults. aims address how these are implemented improve stewardship, age-specific challenges, and manage infection risks vulnerable population while assessing limitations, ethical issues, educational barriers, potential benefits. Recent Findings research highlights DHTs enhance use management among Telehealth has increased access infectious disease specialists stewardship services, with promising outcomes like remote monitoring, telemedicine, AI-driven surveillance may advance outbreak response, predict resistance patterns, personalize therapy. Home diagnostic kits physiological sensors offer convenience but face challenges accuracy, patient education, literacy. Summary Further needed validate optimize for population. Risks such as overprescribing due virtual limitations need additional consideration adults require more attention study technical barriers must be addressed maximize benefits
Язык: Английский
Процитировано
0
Healthcare Analytics, Год журнала: 2025, Номер unknown, С. 100389 - 100389
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
Italian Journal of Medicine, Год журнала: 2024, Номер 18(2)
Опубликована: Апрель 15, 2024
In hospital settings, effective risk management is critical to ensuring patient safety, regulatory compliance, and operational effectiveness. Conventional approaches assessment mitigation frequently rely on manual procedures retroactive analysis, which might not be sufficient recognize respond new risks as they arise. This study examines how artificial intelligence (AI) technologies can improve in healthcare facilities, fortifying safety precautions guidelines while improving the standard of care overall. Hospitals proactively identify mitigate risks, optimize resource allocation, clinical outcomes by utilizing AI-driven predictive analytics, natural language processing, machine learning algorithms. The different applications AI are discussed this paper, along with opportunities, problems, suggestions for their use settings.
Язык: Английский
Процитировано
3
Discover Artificial Intelligence, Год журнала: 2024, Номер 4(1)
Опубликована: Авг. 23, 2024
Abstract This study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep models. The aim is to improve detection processes diagnose diseases effectively. emphasizes importance teamwork harnessing AI’s full potential for image analysis. Collaboration between doctors AI experts crucial developing tools that bridge gap concepts applications. demonstrates effectiveness classifiers, such as forest algorithms models, These techniques enhance accuracy expedite analysis, aiding development accurate medications. evidenced technologically assisted analysis significantly improves efficiency across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. outcomes were supported by reduced diagnosis time. exploration also helps us understand ethical considerations related privacy security data, bias, fairness algorithms, well role consultation ensuring responsible use healthcare.
Язык: Английский
Процитировано
1
Gaziantep Islam Science and Technology University, Год журнала: 2024, Номер 5(4), С. 168 - 181
Опубликована: Июнь 6, 2024
Artificial intelligence (AI) addressed several infectious disease concerns by using its capabilities and acknowledging constraints, with some adjustments clarifications. The research focused on important difficulties related to artificial in diseases. This review advocates for the use of clinical practice research. AI categorises article components such as title, abstract, introduction, method, findings, discussions, which helps scholars save time. speeds up improves scientific writing. Some comments may be misleading or inaccurate, putting accuracy at risk. Current systems provide precise safe responses, but they often lack contextual understanding. diagnostic technologies leads misidentification safety risks. Utilising medical technology ethically requires supervision regulation. institutions have prohibited because inefficacy. assist physicians gathering data patient case studies. Identify control new technologies. ChatGPT other models need more training.
Язык: Английский
Процитировано
0
medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown
Опубликована: Июнь 30, 2024
Abstract Background Artificial Intelligence (AI) has potential to transform healthcare including the field of infectious diseases diagnostics. This study assesses capability three large language models (LLMs), GPT 4, Llama 3, and Gemini 1.5 generate differential diagnoses, comparing their outputs against those medical experts evaluate AI’s in augmenting clinical decision-making. Methods evaluates diagnosis capabilities LLMs, 1.5, using 50 simulated disease cases. The cases were diverse, complex, reflective common scenarios, detailed histories, symptoms, lab results, imaging findings. Each model received standardized case information produced which then compared reference lists created by experts. analysis utilized Jaccard index Kendall’s Tau assess similarity order accuracy, summarizing findings with mean, standard deviation, combined p-values. Results mean numbers diagnoses generated 6.22, 5.06, 10.02 respectively was significantly different (p < 0.001) from Jac-card 0.3, 0.21, 0.24 while 0.4, 0.7, 0.33 respectively. p-value 1, 0.979 indicating no significant association between LLMs Conclusion Although like exhibit varying effectiveness, none align expert-level diagnostic emphasizing need for further development refinement. highlight importance rigorous validation, ethical considerations, seamless integration into workflows ensure AI tools enhance delivery patient outcomes effectively.
Язык: Английский
Процитировано
0
Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 73 - 84
Опубликована: Окт. 8, 2024
Язык: Английский
Процитировано
0