Artificial Intelligence (AI) in Drugs and Pharmaceuticals

Sahu, Adarsh; Mishra, Jyotika; Kushwaha, Namrata

doi:10.2174/1386207325666211207153943

Cited by 38 publications

(19 citation statements)

References 124 publications

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“…[ 17 ] Secondly, artificial intelligence can be used for preclinical drug screening. [ 19 , 20 ] This is a predictive model for developing drugs for diseases, maximizing drug screening before clinical trials, and providing precision medicine solutions for patients with diseases. [ 19 ] Particularly, disease data from preclinical and clinical trials are screened, new data from mouse models are combined, and experimental models guide computational model design through feedback loops to establish prediction models for tumor drug response.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, this is also the learning and analysis based on special data. [19,20] Musculoskeletal ultrasound is currently used to study the blood flow signal of the rotator cuff space, the thickness of the glenohumeral joint capsule, and the thickness of the coracohumeral ligament in patients with scapulohumeral periarthritis. [2] In this study, ultrasound interventional therapy has been performed for a long time, and a good therapeutic effect has been achieved in patients with scapulohumeral periarthritis.…”

Section: Discussionmentioning

confidence: 99%

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Analysis of the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis

Wang

et al. 2023

Medicine

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To explore the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis. A total of 165 patients with periarthritis of the shoulder, who were admitted to our hospital from January 2020 to January 2022, were selected. Konica SONIMAGE HS1 PLUS color Doppler ultrasound diagnostic instrument was used to detect muscles and bones of patients with scapulohumeral periarthritis. This study proposed an intelligent clustering analysis algorithm with musculoskeletal ultrasound parameters. The neural network was trained on a GeForce RTX 3060 with the Adam W optimizer, a batch size of 12, and an initial learning rate of 5E-4. Each batch of 2 types of trained samples was input into the network in a specific proportion. Pain was assessed on a 10-point visual analogue scale. On the affected side of scapulohumeral periarthritis, the mild pain group showed thickening of the shoulder posterior capsule (2.02 ± 0.72) mm with clear edges. In the moderate pain group, the thickness of the shoulder posterior capsule gradually narrowed (1.01 ± 0.38) mm and became even thinner than that of the unaffected side, with irregular and blurred edges. In the severe pain group, the thickness of the shoulder posterior capsule largely returned to normal (1.21 ± 0.42) mm, and the edge was clear. Multivariate logistic regression showed that in addition to musculoskeletal ultrasound parameters, the length of service, work nature, and the busy degree of work of patients with periarthritis of the shoulder were significant factors that influenced shoulder periarthritis pain ( P < .05). The performance of the proposed intelligent auscultation algorithm was further examined in a clinical setting, in which we used 165 clinical musculoskeletal ultrasound samples (including 81 positive and 84 negative samples) as a test set. The accuracy, sensitivity, and specificity were 0.833, 0.872, and 0.801, respectively. Musculoskeletal ultrasound combined with artificial intelligence algorithm is a new diagnostic and staging tool for scapulohumeral periarthritis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Analysis of the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis

Wang

et al. 2023

Medicine

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“…Leveraging AI-driven NLP for participant screening using EHR could substantially reduce trials costs and expedite delivery (22,23). This approach has been actively executed by pharmaceutical companies (24,25).…”

Section: Discussionmentioning

confidence: 99%

Source of data for artificial intelligence applications in vascular surgery - a scoping review

Powezka,

Slater,

Wall

et al. 2023

Preprint

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BackgroundApplications of Artificial Intelligence (AI) are gaining traction in healthcare, including vascular surgery. While most healthcare data is in the form of narrative text or audio recordings, natural language processing (NLP) offers the ability to extract structured information from unstructured medical records. This can be used to develop more accurate risk prediction models, and to identify patients for trials to improve recruitment. The goals of this scoping review were to determine the source of data used to develop AI-based algorithms with emphasis on natural language processing, establish their application in different areas of vascular surgery and identify a target audience of published journals.MethodsA literature search was carried out using PubMed, EMBASE, and Google Scholar database from January 1996 to March 2023. Following screening, 342 peer-reviewed articles met the eligibility criteria.FindingsAmong the 342 articles meeting eligibility criteria, the majority (191) were published after 2020. NLP algorithms were described in 34 papers, while 115 and 193 papers focused on machine learning (ML) and deep learning (DL), respectively. Two-thirds (67.25%) were published in non-clinical journals. The AI-based algorithms found widest application in research related to aorta and its branches (126 articles), followed by carotid disease (85), and peripheral arterial disease (65). Image-based data were utilised in 216 articles, while 153 and 85 papers relied on medical records, and clinical parameters. The AI algorithms were used for predictive modelling (123 papers), medical image segmentation (118), and to aid identification, detection, and diagnosis (103). Only 18 publications aimed to enhance the understanding of vascular disease pathophysiology.InterpretationUtilisation of different data sources and AI technologies depends on the purpose of the undertaken research. Despite the abundance of available of textual data, the NLP is disproportionally underutilised AI sub-domain.FundingNone.

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“…Nevertheless, AI was used in other sectors, e.g., the pharmaceutical industry, including for drug discovery and development (Paul et al 2021). Artificial intelligence is being widely used to improve the time needed for drugs design and to develop new techniques that could be used in the design (Sahu et al 2021). Each step of drug design could be improved using AI technologies.…”

Section: Product Developmentmentioning

confidence: 99%

“…Besides, the integration of AI methods could mean a high success rate in the development of new compounds. In addition, AI is a powerful tool for developing clinical trial output prediction; this further decreases the clinical trials cost by improving the success rate (Sahu et al 2021;Selvaraj et al 2021).…”

Section: Product Developmentmentioning

confidence: 99%

Integrating the new age of bioeconomy and Industry 4.0 into biorefinery process design

Clauser

Felissia

Área

et al. 2022

BioRes

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Manufacturing processes and their economy are dramatically evolving due to machinery and digital control improvements. Artificial intelligence, big data analytics, and the Internet of Things are key tools for this new industrial revolution era based on Industry 4.0. Bioeconomy and circular economy concepts have appeared in the forest, agriculture, food, pharmaceutical, pulp and paper, chemical, biotechnological, and energy areas, etc., to achieve sustainable economic growth development via biomass valorization in a biorefinery platform. Biorefinery process development at an industrial scale requires the previous design and assessment of processes and technologies. Therefore, economic, environmental, and social factors should be evaluated to prevent the failure in one of these issues that could affect the performance of the others. With a growing interest in sustainable economic development, there is a need to incorporate new technologies early enough in the process design. This study aims to better understand how Industry 4.0 era tools can bring new solutions to the biorefinery process design, in terms of the technical, economic, environmental, and social factors. Thus, these tools could improve and revolutionize the process selection optimization, provide alternatives for biomass valorization, integration strategies, and the metrics selection for process evaluation, adding the approach toward sustainable economic development.

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Artificial Intelligence (AI) in Drugs and Pharmaceuticals

Cited by 38 publications

References 124 publications

Analysis of the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis

Analysis of the value of artificial intelligence combined with musculoskeletal ultrasound in the differential diagnosis of pain rehabilitation of scapulohumeral periarthritis

Source of data for artificial intelligence applications in vascular surgery - a scoping review

Integrating the new age of bioeconomy and Industry 4.0 into biorefinery process design

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