Clinical Toxicology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 10
Published: Jan. 14, 2025
Introduction Delayed neurological sequelae is a common complication following carbon monoxide poisoning, which significantly affects the quality of life patients with condition. We aimed to develop machine learning-based prediction model predict frequency delayed in poisoning.
Language: Английский
Franklin Open, Journal Year: 2025, Volume and Issue: unknown, P. 100212 - 100212
Published: Jan. 1, 2025
Language: Английский
Citations
1
Healthcare, Journal Year: 2024, Volume and Issue: 12(7), P. 788 - 788
Published: April 5, 2024
Prescribing medications is a fundamental practice in the management of illnesses that necessitates in-depth knowledge clinical pharmacology. Polypharmacy, or concurrent use multiple by individuals with complex health conditions, poses significant challenges, including an increased risk drug interactions and adverse reactions. The Saudi Vision 2030 prioritises enhancing healthcare quality safety, addressing polypharmacy. Artificial intelligence (AI) offers promising tools to optimise medication plans, predict reactions ensure safety. This review explores AI’s potential revolutionise polypharmacy Arabia, highlighting practical applications, challenges path forward for integration AI solutions into practices.
Language: Английский
Citations
8
Current Oncology, Journal Year: 2024, Volume and Issue: 31(9), P. 5255 - 5290
Published: Sept. 6, 2024
Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning natural language processing, their applications HNC. The integration of with imaging techniques, genomics, electronic health records explored, emphasizing its role early detection, biomarker discovery, planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, real-time monitoring systems are poised to further advance field. Addressing these fostering among experts, clinicians, researchers crucial developing equitable effective applications. future HNC holds significant promise, offering potential breakthroughs diagnostics, personalized therapies, improved patient outcomes.
Language: Английский
Citations
8
Journal of Clinical Medicine, Journal Year: 2024, Volume and Issue: 13(15), P. 4337 - 4337
Published: July 25, 2024
BC, affecting both women and men, is a complex disease where early diagnosis plays crucial role in successful treatment enhances patient survival rates. The Metaverse, virtual world, may offer new, personalized approaches to diagnosing treating BC. Although Artificial Intelligence (AI) still its stages, rapid advancement indicates potential applications within the healthcare sector, including consolidating information one accessible location. This could provide physicians with more comprehensive insights into details. Leveraging Metaverse facilitate clinical data analysis improve precision of diagnosis, potentially allowing for tailored treatments BC patients. However, while this article highlights possible transformative impacts technologies on treatment, it important approach these developments cautious optimism, recognizing need further research validation ensure enhanced care greater accuracy efficiency.
Language: Английский
Citations
7
Future Healthcare Journal, Journal Year: 2024, Volume and Issue: 11(3), P. 100182 - 100182
Published: Sept. 1, 2024
The presence of artificial intelligence (AI) in healthcare is a powerful and game-changing force that completely transforming the industry as whole. Using sophisticated algorithms data analytics, AI has unparalleled prospects for improving patient care, streamlining operational efficiency, fostering innovation across ecosystem. This study conducts comprehensive bibliometric analysis research on healthcare, utilising SCOPUS database primary source.
Language: Английский
Citations
7
Frontiers in Health Services, Journal Year: 2024, Volume and Issue: 4
Published: June 11, 2024
Background Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, experience and patient preferences. Despite popularization EBP, research has shown that there are many barriers to achieving the goals EBP model. The use artificial intelligence (AI) in healthcare been proposed as a means improve decision-making. aim this paper was pinpoint key challenges pertaining pillars investigate potential AI surmounting these contributing more evidence-based practice. We conducted selective review literature integration achieve this. Challenges with components Clinical decision-making line model presents several challenges. availability existence robust evidence sometimes pose limitations due slow generation dissemination processes, well scarcity high-quality evidence. Direct application is not always viable because studies often involve groups distinct from those encountered routine healthcare. Clinicians need rely their interpret relevance contextualize it within unique needs patients. Moreover, might be influenced by cognitive implicit biases. Achieving involvement shared between clinicians patients remains challenging factors such low levels health literacy among reluctance actively participate, rooted clinicians' attitudes, scepticism towards knowledge ineffective communication strategies, busy environments limited resources. assistance for promising solution address inherent process, conducting studies, generating synthesizing findings, disseminating crucial information implementing findings into systems have advantage over human processing specific types data information. great promise areas image analysis. avenues enhance engagement saving time increase autonomy although lack issue. Conclusion This underscores AI's augment practices, potentially marking emergence 2.0. However, also uncertainties regarding how will contribute Hence, empirical essential validate substantiate various aspects
Language: Английский
Citations
6
Journal of Multidisciplinary Healthcare, Journal Year: 2024, Volume and Issue: Volume 17, P. 4011 - 4022
Published: Aug. 1, 2024
Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into systems, it promises advancements across various domains. This review explores diverse applications of in healthcare, along with challenges limitations that need addressed. The aim is provide a comprehensive overview AI's impact on identify areas further development focus.
Language: Английский
Citations
5
Healthcare, Journal Year: 2024, Volume and Issue: 12(17), P. 1730 - 1730
Published: Aug. 30, 2024
Artificial Intelligence (AI) has shown remarkable potential to revolutionise healthcare by enhancing diagnostics, improving treatment outcomes, and streamlining administrative processes. In the global regulatory landscape, several countries are working on regulating AI in healthcare. There five key issues that need be addressed: (i) data security protection—measures cover “digital health footprints” left unknowingly patients when they access services; (ii) quality—availability of safe secure more open database sources for AI, algorithms, datasets ensure equity prevent demographic bias; (iii) validation algorithms—mapping explainability causability system; (iv) accountability—whether this lies with professional, organisation, or personified algorithm; (v) ethics equitable access—whether fundamental rights people met an ethical manner. Policymakers may consider entire life cycle services databases were used training system, along requirements their risk assessments publicly accessible effective oversight. enhance functionality over time undergo repeated algorithmic impact assessment must also demonstrate real-time performance. Harmonising frameworks at international level would help resolve cross-border services.
Language: Английский
Citations
5
Cureus, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 16, 2024
Maternal health remains a critical global challenge, with disparities in access to care and quality of services contributing high maternal mortality morbidity rates. Artificial intelligence (AI) has emerged as promising tool for addressing these challenges by enhancing diagnostic accuracy, improving patient monitoring, expanding care. This review explores the transformative role AI healthcare, focusing on its applications early detection pregnancy complications, personalized care, remote monitoring through AI-driven technologies. tools such predictive analytics machine learning can help identify at-risk pregnancies guide timely interventions, reducing preventable neonatal complications. Additionally, AI-enabled telemedicine virtual assistants are bridging healthcare gaps, particularly underserved rural areas, accessibility women who might otherwise face barriers Despite potential benefits, data privacy, algorithmic bias, need human oversight must be carefully addressed. The also discusses future research directions, including globally ethical frameworks integration. holds revolutionize both accessibility, offering pathway safer, more equitable outcomes.
Language: Английский
Citations
5
American Journal of Physical Medicine & Rehabilitation, Journal Year: 2024, Volume and Issue: 103(11), P. 1039 - 1044
Published: July 15, 2024
Artificial intelligence emerges as a transformative force, offering novel solutions to enhance medical education and mentorship in the specialty of physical medicine rehabilitation. is technology that being adopted nearly every industry. In medicine, use artificial growing. may also assist with some challenges mentorship, including limited availability experienced mentors, logistical difficulties time geography are constraints traditional mentorship. this commentary, we discuss various models mentoring, expert systems, conversational agents, hybrid models. These enable tailored guidance, broaden outreach within rehabilitation community, support continuous learning development. Balancing intelligence's technical advantages essential human elements while addressing ethical considerations, integration into presents paradigm shift toward more accessible, responsive, enriched experience medicine.
Language: Английский
Citations
4