Journal of Medical Internet Research, Год журнала: 2024, Номер 26, С. e54556 - e54556
Опубликована: Июль 15, 2024
The leaders of health care organizations are grappling with rising expenses and surging demands for services. In response, they increasingly embracing artificial intelligence (AI) technologies to improve patient delivery, alleviate operational burdens, efficiently safety quality.
Язык: Английский
Cureus, Год журнала: 2023, Номер unknown
Опубликована: Фев. 20, 2023
Background Artificial intelligence (AI) is evolving for healthcare services. Higher cognitive thinking in AI refers to the ability of system perform advanced processes, such as problem-solving, decision-making, reasoning, and perception. This type goes beyond simple data processing involves understand manipulate abstract concepts, interpret, use information a contextually relevant way, generate new insights based on past experiences accumulated knowledge. Natural language models like ChatGPT conversational program that can interact with humans provide answers queries. Objective We aimed ascertain capability solving higher-order reasoning subject pathology. Methods cross-sectional study was conducted internet using an AI-based chat provides free service research purposes. The current version (January 30 version) used converse total 100 These questions were randomly selected from question bank institution categorized according different systems. responses each collected stored further analysis. evaluated by three expert pathologists zero five scale into structure observed learning outcome (SOLO) taxonomy categories. score compared one-sample median test hypothetical values find its accuracy. Result A solved average 45.31±7.14 seconds answer. overall 4.08 (Q1-Q3: 4-4.33) which below maximum value (one-test p <0.0001) similar four = 0.14). majority (86%) "relational" category SOLO taxonomy. There no difference scores asked various organ systems Pathology (Kruskal Wallis 0.55). rated had excellent level inter-rater reliability (ICC 0.975 [95% CI: 0.965-0.983]; F 40.26; < 0.0001). Conclusion solve pathology relational Hence, text output connections among parts meaningful response. approximately 80%. academicians or students get help reasoning-type also. As evolving, studies are needed accuracy any versions.
Язык: Английский
Процитировано
164
Global Journal of Flexible Systems Management, Год журнала: 2023, Номер 24(4), С. 659 - 689
Опубликована: Сен. 28, 2023
Язык: Английский
Процитировано
86
Journal of Medical Internet Research, Год журнала: 2023, Номер 25, С. e43110 - e43110
Опубликована: Янв. 27, 2023
Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation artificial intelligence research in radiology, provided that these models have sufficient medical domain knowledge. Herein, we show has learned relevant representations of x-ray images, with capabilities terms zero-shot text-to-image generation new the continuation an beyond its original boundaries, removal elements; however, images pathological abnormalities (eg, tumors, fractures, inflammation) or computed tomography, magnetic resonance imaging, ultrasound are still limited. The use generative augmenting generating radiological data thus seems feasible, even if further fine-tuning adaptation to their respective domains required first.
Язык: Английский
Процитировано
56
Cureus, Год журнала: 2023, Номер unknown
Опубликована: Апрель 2, 2023
Background Healthcare-related artificial intelligence (AI) is developing. The capacity of the system to carry out sophisticated cognitive processes, such as problem-solving, decision-making, reasoning, and perceiving, referred higher thinking in AI. This kind requires more than just processing facts; it also entails comprehending working with abstract ideas, evaluating applying data relevant context, producing new insights based on prior learning experience. ChatGPT an intelligence-based conversational software that can engage people answer questions uses natural language models. platform has created a worldwide buzz keeps setting ongoing trend solving many complex problems various dimensions. Nevertheless, ChatGPT's correctly respond queries requiring higher-level medical biochemistry not yet been investigated. So, this research aimed evaluate aptitude for responding higher-order biochemistry. Objective In study, our objective was determine whether address related biochemistry. Methods cross-sectional study done online by conversing current version (14 March 2023, which presently free registered users). It presented 200 reasoning require thinking. These were randomly picked from institution's question bank classified according Competency-Based Medical Education (CBME) curriculum's competency modules. responses collected archived subsequent research. Two expert academicians examined replies zero five scale. score's accuracy determined one-sample Wilcoxon signed rank test using hypothetical values. Result AI answered median score 4.0 (Q1=3.50, Q3=4.50). Using single sample test, result less maximum (p=0.001) comparable four (p=0.16). There no difference different CBME modules (Kruskal-Wallis p=0.39). inter-rater reliability scores scored two faculty members outstanding (ICC=0.926 (95% CI: 0.814-0.971); F=19; p=0.001) Conclusion results indicate potential be successful tool answering biochemistry, five. However, continuous training development recent advances are essential improve performance make functional ever-growing field academic usage.
Язык: Английский
Процитировано
54
Cureus, Год журнала: 2023, Номер unknown
Опубликована: Июнь 26, 2023
Background Artificial intelligence (AI) is evolving in the medical education system. ChatGPT, Google Bard, and Microsoft Bing are AI-based models that can solve problems education. However, applicability of AI to create reasoning-based multiple-choice questions (MCQs) field physiology yet be explored. Objective We aimed assess compare generating MCQs for MBBS (Bachelor Medicine, Bachelor Surgery) undergraduate students on subject physiology. Methods The National Medical Commission India has developed an 11-module curriculum with various competencies. Two physiologists independently chose a competency from each module. third physiologist prompted all three AIs generate five chosen competency. two who provided competencies rated generated by scale 0-3 validity, difficulty, reasoning ability required answer them. analyzed average scores using Kruskal-Wallis test distribution across total module-wise responses, followed post-hoc pairwise comparisons. used Cohen's Kappa (Κ) agreement between raters. expressed data as median interquartile range. determined their statistical significance p-value <0.05. Results ChatGPT Bard 110 only 100 it failed them validity was 3 (3-3) (1.5-3) Bing, showing significant difference (p<0.001) among models. difficulty 1 (0-1) (1-2) (p=0.006). no (p=0.235). K ≥ 0.8 parameters Conclusion still needs evolve showed certain limitations. significantly least valid MCQs, while difficult MCQs.
Язык: Английский
Процитировано
50
Neurocomputing, Год журнала: 2024, Номер 599, С. 128111 - 128111
Опубликована: Сен. 1, 2024
Язык: Английский
Процитировано
20
PLoS ONE, Год журнала: 2024, Номер 19(8), С. e0305949 - e0305949
Опубликована: Авг. 9, 2024
Implementation of artificial intelligence systems for healthcare is challenging. Understanding the barriers and implementation strategies can impact their adoption allows better anticipation planning. This study's objective was to create a detailed inventory AI in support advancements methods processes healthcare. A sequential explanatory mixed method design used. Firstly, scoping reviews systematic literature were identified using PubMed. Selected studies included empirical cases use clinical practice. As deemed insufficient fulfil aim study, data collection shifted primary those reviews. The screened by title abstract, thereafter read full text. Then, on extracted from articles, thematically coded inductive analysis, summarized. Subsequently, direct qualitative content analysis 69 interviews with leaders professionals confirmed added results review. Thirty-eight six met inclusion exclusion criteria. Barriers grouped under three phases (planning, implementing, sustaining use) categorized into eleven concepts; Leadership, Buy-in, Change management, Engagement, Workflow, Finance human resources, Legal, Training, Data, Evaluation monitoring, Maintenance. Ethics emerged as twelfth concept through interviews. study illustrates inherent challenges useful implementing Future research should explore various aspects leadership, collaboration contracts among key stakeholders, legal surrounding clinicians' liability, solutions ethical dilemmas, infrastructure efficient integration workflows, define decision points process.
Язык: Английский
Процитировано
19
Hepatology International, Год журнала: 2025, Номер unknown
Опубликована: Фев. 27, 2025
Язык: Английский
Процитировано
4
Deleted Journal, Год журнала: 2022, Номер 1, С. 9 - 9
Опубликована: Сен. 7, 2022
Introduction: artificial intelligence and machine learning have brought significant changes transformed everyday life, this is also seen in healthcare medicine. A bibliographic review was carried out with the aim of delving into current future applications health biomedical sciences sector.Methods: a main databases other search services. The terms “artificial intelligence”, “automated learning”, “deep “health sciences” were used, as well descriptors.Results: (AI) models are playing an increasingly important role research clinical practice, showing their potential various applications, such risk modeling stratification, personalized screening, diagnosis (including classification molecular disease subtypes), prediction response to therapy, prognosis. All these fields could greatly improve trend towards precision medicine, resulting more reliable approaches high impact on diagnostic therapeutic pathways. This implies paradigm shift from defining statistical population perspectives individual predictions, allowing for effective preventive actions therapy planning.Conclusions: there application large scale
Язык: Английский
Процитировано
41
Cureus, Год журнала: 2023, Номер unknown
Опубликована: Июнь 30, 2023
Artificial intelligence (AI) has emerged as a transformative technology in various industries, and its potential dentistry is gaining significant attention. This abstract explores the future prospects of AI dentistry, highlighting to revolutionize clinical practice, improve patient outcomes, enhance overall efficiency dental care. The application encompasses several key areas, including diagnosis, treatment planning, image analysis, management, personalized algorithms have shown promising results automated detection diagnosis conditions, such caries, periodontal diseases, oral cancers, aiding clinicians early intervention improving outcomes. Furthermore, AI-powered planning systems leverage machine learning techniques analyze vast amounts data, considering factors like medical history, anatomical variations, success rates. These provide dentists with valuable insights support making evidence-based decisions, ultimately leading more predictable tailored approaches. While immense, it essential address certain challenges, data privacy, algorithm bias, regulatory considerations. Collaborative efforts between professionals, experts, policymakers are crucial developing robust frameworks that ensure responsible ethical implementation dentistry. Moreover, AI-driven robotics introduced innovative approaches surgery, enabling precise minimally invasive procedures, reducing discomfort recovery time. Virtual reality (VR) augmented (AR) applications further education training, allowing professionals refine their skills realistic immersive environment. holds tremendous promise shaping Through ability accurate diagnoses, facilitate streamline enable care, practice significantly Embracing this development will undoubtedly field fostering efficient, precise, patient-centric approach healthcare. Overall, represents powerful tool aspects society, from healthcare outcomes optimizing business operations. Continued research, development, technologies shape our future, unlocking new possibilities transforming way we live work.
Язык: Английский
Процитировано
39