Asian Journal of Psychiatry, Год журнала: 2023, Номер 89, С. 103770 - 103770
Опубликована: Сен. 20, 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
Medicinal Research Reviews, Год журнала: 2020, Номер 41(3), С. 1427 - 1473
Опубликована: Дек. 9, 2020
Abstract Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) remains the most challenging area drug discovery, accompanied with long timelines and high attrition rates. With rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) machine learning (ML) have emerged as an indispensable tool to draw meaningful insights improve decision making discovery. Thanks advancements AI ML algorithms, now AI/ML‐driven solutions unprecedented potential accelerate process CNS discovery better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical efforts their implementations area. After introducing AI/ML models well conceptualization preparation, outline applications technologies several key procedures including target identification, compound screening, hit/lead generation optimization, response synergy prediction, de novo design, repurposing. We review current state‐of‐the‐art AI/ML‐guided focusing on blood–brain barrier permeability prediction implementation into neurological diseases. Finally, discuss major challenges limitations approaches possible future directions that may provide resolutions these difficulties.
Язык: Английский
Процитировано
273
Biological Psychiatry Cognitive Neuroscience and Neuroimaging, Год журнала: 2021, Номер 6(9), С. 856 - 864
Опубликована: Фев. 9, 2021
Язык: Английский
Процитировано
262
Current Opinion in Psychology, Год журнала: 2020, Номер 36, С. 112 - 117
Опубликована: Июнь 3, 2020
Язык: Английский
Процитировано
235
Research in Social and Administrative Pharmacy, Год журнала: 2023, Номер 19(8), С. 1236 - 1242
Опубликована: Июнь 4, 2023
Artificial Intelligence (AI) has revolutionized various domains, including education and research. Natural language processing (NLP) techniques large models (LLMs) such as GPT-4 BARD have significantly advanced our comprehension application of AI in these fields. This paper provides an in-depth introduction to AI, NLP, LLMs, discussing their potential impact on By exploring the advantages, challenges, innovative applications technologies, this review gives educators, researchers, students, readers a comprehensive view how could shape educational research practices future, ultimately leading improved outcomes. Key discussed field include text generation, data analysis interpretation, literature review, formatting editing, peer review. academics support constructive feedback, assessment, grading, tailored curricula, personalized career guidance, mental health support. Addressing challenges associated with ethical concerns algorithmic biases, is essential for maximizing improve Ultimately, aims contribute ongoing discussion about role highlight its lead better outcomes researchers.
Язык: Английский
Процитировано
218
Bioengineering, Год журнала: 2024, Номер 11(4), С. 337 - 337
Опубликована: Март 29, 2024
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging a key force transformation. This review motivated by urgent need to harness AI’s potential mitigate these issues aims critically assess integration in different domains. We explore how AI empowers clinical decision-making, optimizes hospital operation management, refines medical image analysis, revolutionizes patient care monitoring through AI-powered wearables. Through several case studies, we has transformed specific domains discuss remaining possible solutions. Additionally, will methodologies assessing solutions, ethical of deployment, importance data privacy bias mitigation responsible technology use. By presenting critical assessment transformative potential, this equips researchers with deeper understanding current future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, technologists navigate complexities implementation, fostering development AI-driven solutions that prioritize standards, equity, patient-centered approach.
Язык: Английский
Процитировано
213
Psychotherapy Research, Год журнала: 2020, Номер 31(1), С. 92 - 116
Опубликована: Авг. 29, 2020
Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts data. This scoping review paper aims broadly explore the nature research activity using ML in context psychological talk therapies, highlighting scope current methods considerations for clinical practice directions future research. Using a systematic search methodology, fifty-one studies were identified. A narrative synthesis indicates two types studies, those who developed tested an model (k=44), reported on feasibility particular treatment tool uses algorithm (k=7). Most development used supervised classify or predict labeled process outcome data, whereas others unsupervised identify clusters unlabeled patient Overall, applications psychotherapy demonstrated range possible benefits indications process, adherence, therapist skills response prediction, as well ways accelerate through automated behavioral linguistic coding. Given novelty potential this field, these proof-of-concept are encouraging, however, do not necessarily translate improved (yet).
Язык: Английский
Процитировано
151
Psychiatry Research, Год журнала: 2019, Номер 284, С. 112732 - 112732
Опубликована: Дек. 9, 2019
Язык: Английский
Процитировано
150
Frontiers in Digital Health, Год журнала: 2021, Номер 3
Опубликована: Сен. 6, 2021
Mental health disorders are complex of the nervous system characterized by a behavioral or mental pattern that causes significant distress impairment personal functioning. illness is particular concern for younger people. The WHO estimates around 20% world's children and adolescents have condition, rate almost double compared to general population. One approach toward mitigating medical socio-economic effects leveraging power digital technology deploy assistive, preventative, therapeutic solutions people in need. We define "digital health" as any application assessment, support, prevention, treatment. However, there only limited evidence tools can be successfully implemented clinical settings. Authors pointed lack technical standards apps, personalized neurotechnology, assistive cognitive possible cause suboptimal adoption implementation setting. Further, ethical concerns been raised related insufficient effectiveness, adequate validation, user-centered design well data privacy vulnerabilities current products. aim this paper report on scoping review we conducted capture synthesize growing literature promises challenges young aged 0-25. This seeks survey scope focus relevant literature, identify major benefits opportunities significance (e.g., reducing suffering improving well-being), provide comprehensive mapping emerging challenges. Our findings synthesis offer detailed informative basis stakeholder involved development, deployment, management ethically-aligned
Язык: Английский
Процитировано
145
The Lancet Digital Health, Год журнала: 2022, Номер 4(11), С. e829 - e840
Опубликована: Окт. 10, 2022
In this Series paper, we explore the promises and challenges of artificial intelligence (AI)-based precision medicine tools in mental health care from clinical, ethical, regulatory perspectives. The real-world implementation these is increasingly considered prime solution for key issues health, such as delayed, inaccurate, inefficient delivery. Similarly, machine-learning-based empirical strategies are becoming commonplace psychiatric research because their potential to adequately deconstruct biopsychosocial complexity disorders, hence improve nosology prognostic preventive paradigms. However, steps needed translate into practice currently hampered by multiple interacting challenges. These obstructions range current technology-distant state clinical practice, over lack valid databases required feed data-intensive AI algorithms, model development validation considerations being disconnected core principles utility ethical acceptability. provide recommendations on how could be addressed an interdisciplinary perspective pave way towards a framework care, leveraging combined strengths human AI.
Язык: Английский
Процитировано
136