Neurosurgical Review, Год журнала: 2023, Номер 46(1)
Опубликована: Май 16, 2023
Язык: Английский
BMC Medical Education, Год журнала: 2023, Номер 23(1)
Опубликована: Сен. 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.
Язык: Английский
Процитировано
1226
International Journal of Nursing Studies, Год журнала: 2024, Номер 154, С. 104753 - 104753
Опубликована: Март 13, 2024
Язык: Английский
Процитировано
27
NPP—Digital Psychiatry and Neuroscience, Год журнала: 2024, Номер 2(1)
Опубликована: Май 24, 2024
Abstract The integration of large language models (LLMs) into mental healthcare and research heralds a potentially transformative shift, one offering enhanced access to care, efficient data collection, innovative therapeutic tools. This paper reviews the development, function, burgeoning use LLMs in psychiatry, highlighting their potential enhance through improved diagnostic accuracy, personalized streamlined administrative processes. It is also acknowledged that introduce challenges related computational demands, for misinterpretation, ethical concerns, necessitating development pragmatic frameworks ensure safe deployment. We explore both promise enriching psychiatric care examples such as predictive analytics therapy chatbots risks including labor substitution, privacy necessity responsible AI practices. conclude by advocating processes develop guardrails, red-teaming, multi-stakeholder-oriented safety, guidelines/frameworks, mitigate harness full advancing health.
Язык: Английский
Процитировано
23
Journal of Medical Internet Research, Год журнала: 2025, Номер 27, С. e56306 - e56306
Опубликована: Фев. 19, 2025
Background The integration of artificial intelligence (AI) into health care has become a crucial element in the digital transformation systems worldwide. Despite potential benefits across diverse medical domains, significant barrier to successful adoption AI applications remains prevailing low user trust these technologies. Crucially, this challenge is exacerbated by lack consensus among experts from different disciplines on definition within sector. Objective We aimed provide first consensus-based analysis based an interdisciplinary panel domains. Our findings can be used address problem defining applications, fostering discussion concrete real-world scenarios which humans interact with explicitly. Methods combination framework and 3-step process involving 18 international fields computer science, medicine, philosophy technology, ethics, social sciences. consisted synchronous phase during expert workshop where we discussed notion defined initial important elements guide our analysis, agreed 5 case studies. This was followed 2-step iterative, asynchronous authors further developed, discussed, refined notions respect specific cases. Results identified key contextual factors trust, namely, system’s environment, actors involved, framing factors, analyzed causes effects care. revealed that certain were applicable all cases yet also pointed need for fine-grained, multidisciplinary bridging human-centered technology-centered approaches. While regulatory boundaries technological design features are critical implementation care, ultimately, communication positive lived experiences will at forefront trust. allowed us formulate recommendations future research applications. Conclusions paper advocates more nuanced conceptual understanding context By synthesizing insights commonalities differences studies, establishes foundational basis debates discussions trusting
Язык: Английский
Процитировано
3
Artificial Intelligence in Medicine, Год журнала: 2023, Номер 146, С. 102693 - 102693
Опубликована: Ноя. 2, 2023
Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains healthcare, but whether can benefit from AI remains unclear. We conducted a systematic review AI-supported physical technology tested in setting to understand: 1) technology; 2) effect; 3) barriers facilitators implementation. searched MEDLINE, EMBASE, CINAHL, Science Citation Index (Web Science), CIRRIE (now NARIC), OpenGrey. identified 9054 articles included 28 projects. solutions spanned five categories: App-based systems, robotic devices that replace restore gaming systems wearables. randomised controlled trials (RCTs), which evaluated outcomes relating activity, pain, health-related quality life. The effects were inconsistent. Implementation literacy, reliability, user fatigue. Enablers greater access programmes, remote monitoring progress, reduction manpower requirements lower cost. Applications are growing field, yet be studied rigorously. Developers must strive conduct robust evaluations real-world appraise post implementation experiences.
Язык: Английский
Процитировано
40
Journal of Medical Internet Research, Год журнала: 2023, Номер 25, С. e48763 - e48763
Опубликована: Авг. 31, 2023
The reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand replicate such studies. To address this issue, multiple consensus expert guidelines for ML have been published. However, these cover different parts the analytics lifecycle, individually, none them provide a complete set requirements.
Язык: Английский
Процитировано
29
Journal of the American Medical Informatics Association, Год журнала: 2023, Номер 30(9), С. 1503 - 1515
Опубликована: Май 19, 2023
To derive a comprehensive implementation framework for clinical AI models within hospitals informed by existing frameworks and integrated with reporting standards research.
Язык: Английский
Процитировано
26
Clinical Orthopaedics and Related Research, Год журнала: 2024, Номер 482(4), С. 578 - 588
Опубликована: Март 1, 2024
Background The lay public is increasingly using ChatGPT (a large language model) as a source of medical information. Traditional search engines such Google provide several distinct responses to each query and indicate the for response, but provides in paragraph form prose without providing sources used, which makes it difficult or impossible ascertain whether those are reliable. One practical method infer used by text network analysis. By understanding how uses information relation traditional engines, physicians physician organizations can better counsel patients on use this new tool. Questions/purposes (1) In terms key content words, similar Search queries related topics orthopaedic surgery? (2) Does distribution (academic, governmental, commercial, scientific manuscript) differ based topic’s level consensus, reflected similarity between responses? (3) Do these results vary different versions ChatGPT? Methods We evaluated three relating conditions: “What cause carpal tunnel syndrome?,” tennis elbow?,” “Platelet-rich plasma thumb arthritis?” These were selected because their relatively high, medium, low consensus evidence, respectively. Each question was posed version 3.5 4.0 20 times total 120 responses. Text analysis term frequency–inverse document frequency (TF-IDF) compare from Search. field retrieval, TF-IDF weighted statistical measure importance word collection documents. Higher scores greater two sources. most often rank Using type analysis, be determined calculating summing all keywords response comparing with result assess other. way, relative similarity. To answer our first question, we characterized finding question. scores, could results. reference point interpreting values, generated randomized samples same random sample, values statistically significant obtained chance, allowed us test an appropriate quantitative second classified understand sourcing. more information, gives only single query. So, principally driven one four categories: academic, government, material that took manuscript not peer-reviewed indexed government site (such PubMed). then compared category. third research repeated both analyses when versus 4.0. Results dominated top result. For example, syndrome, academic website mean 7.2. A observed other topics. randomly sample would have 2.7 ± 1.9, controlling length keyword distribution. higher than samples, supporting claim When distribution, common category subject where there strong (carpal syndrome), high-quality rather lower-quality commercial (TF-IDF 8.6 2.2). paralleled websites higher-quality 14.6 0.2). had (mean increase 0.80 0.91; p < 0.001). still Conclusion individual surgery, substantially topic. widely accepted therefore more. fewer available, especially platelet-rich plasma, appears relied heavily small number nonacademic findings persisted even updated Clinical Relevance Physicians should aware likely specific main difference draw upon multiple create aggregate while maintains its distinctness quality sources, much chance will less-reliable case take time educate topic resources give reliable Physician make clear evidence limited so reflect lack evidence.
Язык: Английский
Процитировано
16
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Янв. 9, 2025
Abstract We developed an AI system capable of automatically classifying anterior eye images as either normal or indicative corneal diseases. This study aims to investigate the influence AI’s misleading guidance on ophthalmologists’ responses. cross-sectional included 30 cases each infectious and immunological keratitis. Responses regarding presence infection were collected from 7 specialists 16 non-corneal-specialist ophthalmologists, first based alone then after presenting classification results. The diagnoses deliberately altered present a correct in 70% incorrect 30%. overall accuracy ophthalmologists did not significantly change assistance was introduced [75.2 ± 8.1%, 75.9 7.2%, respectively ( P = 0.59)]. In where presented diagnoses, before showing no significant [60.3 35.2% 53.2 30.9%, 0.11)]. contrast, for non-corneal dropped 54.5 27.8% 31.6 29.3% < 0.001), especially options. Less experienced misled due guidance, but not. Even with introduction diagnostic support systems, importance ophthalmologist’s experience remains crucial.
Язык: Английский
Процитировано
2
Cureus, Год журнала: 2025, Номер unknown
Опубликована: Янв. 16, 2025
Artificial intelligence (AI) has rapidly emerged as a transformative force in medicine, revolutionizing various aspects of healthcare from diagnostics and treatment to public health patient care. This narrative review synthesizes evidence diverse study designs, exploring the current future applications AI medicine. We highlight AI's role improving diagnostic accuracy, optimizing strategies, enhancing care through personalized interventions remote monitoring, drawing upon recent advancements landmark studies. Emerging trends such explainable federated learning are also examined. While acknowledging tremendous potential addresses barriers ethical challenges that need be overcome, including concerns about algorithmic bias, transparency, over-reliance, impact on workforce. emphasize importance establishing regulatory guidelines, fostering collaboration between clinicians developers, ensuring ongoing education for professionals. Despite these challenges, medicine holds immense promise, with significantly improve outcomes, transform delivery, address disparities.
Язык: Английский
Процитировано
1