Biomaterials and Artificial Intelligence: Predictive Modeling and Design
The emergence of artificial intelligence (AI) with synergistic integration is currently a paradigm-shifting strategy for the direction of biomaterials development and design. This paper analyzes the connection between AI and biomaterials, explaining the significant influence of predictive modelling...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/35/e3sconf_icarae2023_01003.pdf |
| _version_ | 1797235284283228160 |
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| author | Rajitha Akula Kansal Lavish Raj Gowtham Kalra Ravi Dhamija Koushal Abdul-Zahra Dalael Saad |
| author_facet | Rajitha Akula Kansal Lavish Raj Gowtham Kalra Ravi Dhamija Koushal Abdul-Zahra Dalael Saad |
| author_sort | Rajitha Akula |
| collection | DOAJ |
| description | The emergence of artificial intelligence (AI) with synergistic integration is currently a paradigm-shifting strategy for the direction of biomaterials development and design. This paper analyzes the connection between AI and biomaterials, explaining the significant influence of predictive modelling on the path of the area. By carefully investigating state-of-the-art studies and unique applications, it illustrates how AI-driven predictive modelling redefined biomaterial design and entered a new era of unusual accuracy and productivity. This research covers a wide variety of AI technologies, from deep neural networks to machine learning, that facilitates the development of prediction models that use large datasets to anticipate the behaviour, characteristics, and interactions of biomaterials. It examines how artificial intelligence (AI) may speed up the method of screening for viable materials, improve their qualities, and forecast there in vivo reactions. This can help translate beachside discoveries into clinical applications more quickly. This paper further explains the future prospects and problems in the field of biomaterials and AI integration, underlining the significance of interdisciplinary working together, standardization of data, and ethical concerns. |
| first_indexed | 2024-04-24T16:45:31Z |
| format | Article |
| id | doaj.art-db47996036374a7f84ed172809b73f63 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-24T16:45:31Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-db47996036374a7f84ed172809b73f632024-03-29T08:30:16ZengEDP SciencesE3S Web of Conferences2267-12422024-01-015050100310.1051/e3sconf/202450501003e3sconf_icarae2023_01003Biomaterials and Artificial Intelligence: Predictive Modeling and DesignRajitha Akula0Kansal Lavish1Raj Gowtham2Kalra Ravi3Dhamija Koushal4Abdul-Zahra Dalael Saad5Institute of Aeronautical EngineeringLovely Professional UniversityDepartment of Mechanical Engineering, New Horizon College of EngineeringLloyd Institute of Engineering & TechnologyLloyd Institute of Management and TechnologyDepartment of medical physicas, Hilla University CollegeThe emergence of artificial intelligence (AI) with synergistic integration is currently a paradigm-shifting strategy for the direction of biomaterials development and design. This paper analyzes the connection between AI and biomaterials, explaining the significant influence of predictive modelling on the path of the area. By carefully investigating state-of-the-art studies and unique applications, it illustrates how AI-driven predictive modelling redefined biomaterial design and entered a new era of unusual accuracy and productivity. This research covers a wide variety of AI technologies, from deep neural networks to machine learning, that facilitates the development of prediction models that use large datasets to anticipate the behaviour, characteristics, and interactions of biomaterials. It examines how artificial intelligence (AI) may speed up the method of screening for viable materials, improve their qualities, and forecast there in vivo reactions. This can help translate beachside discoveries into clinical applications more quickly. This paper further explains the future prospects and problems in the field of biomaterials and AI integration, underlining the significance of interdisciplinary working together, standardization of data, and ethical concerns.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/35/e3sconf_icarae2023_01003.pdfbiomaterialsartificial intelligence (ai)predictive modellingdata analysisdeep neural networksproductivity |
| spellingShingle | Rajitha Akula Kansal Lavish Raj Gowtham Kalra Ravi Dhamija Koushal Abdul-Zahra Dalael Saad Biomaterials and Artificial Intelligence: Predictive Modeling and Design E3S Web of Conferences biomaterials artificial intelligence (ai) predictive modelling data analysis deep neural networks productivity |
| title | Biomaterials and Artificial Intelligence: Predictive Modeling and Design |
| title_full | Biomaterials and Artificial Intelligence: Predictive Modeling and Design |
| title_fullStr | Biomaterials and Artificial Intelligence: Predictive Modeling and Design |
| title_full_unstemmed | Biomaterials and Artificial Intelligence: Predictive Modeling and Design |
| title_short | Biomaterials and Artificial Intelligence: Predictive Modeling and Design |
| title_sort | biomaterials and artificial intelligence predictive modeling and design |
| topic | biomaterials artificial intelligence (ai) predictive modelling data analysis deep neural networks productivity |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/35/e3sconf_icarae2023_01003.pdf |
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