Application of Artificial Intelligence and Machine Learning in Quality Assurance
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2025
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| author | Momin Anam Rafik*, Chavan Shraddha Mitthu, Dr. Datkhile Sachin Vitthal, Dr. Lokhande Rahul Prakash |
| author_facet | Momin Anam Rafik*, Chavan Shraddha Mitthu, Dr. Datkhile Sachin Vitthal, Dr. Lokhande Rahul Prakash |
| contents | <p><span>Artificial intelligence (AI) technology is experiencing rapid growth in various fields due to advancements in computers and technology. AI has also led to the development of several techniques for automated segmentation and planning in the radiotherapy treatment process, greatly improving overall treatment effectiveness.[A]. There have been numerous reports of AI-based applications in machine and patient-specific QA, including predictions for machine beam data or gamma passing rates on IMRT or VMAT plans. Moreover, the development of these technologies is being pursued for multicenter studies. Radiotherapy must have machine- and patient-specific quality assurance (QA) to ensure safety and accuracy. High-precision radiotherapy, including IMRT and VMAT, has become increasingly difficult to manage on the QA level. This paper will explore the role of Artificial Intelligence in software testing.' Quality Assurance in the new age will be greatly influenced by Artificial Intelligence, as it can significantly reduce time and increase efficiency for developing advanced software. </span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14950226 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Application of Artificial Intelligence and Machine Learning in Quality Assurance Momin Anam Rafik*, Chavan Shraddha Mitthu, Dr. Datkhile Sachin Vitthal, Dr. Lokhande Rahul Prakash Artificial Intelligence (AI), Radiotherapy, Quality Assurance (QA), IMRT/VMAT, Automated Segmentation <p><span>Artificial intelligence (AI) technology is experiencing rapid growth in various fields due to advancements in computers and technology. AI has also led to the development of several techniques for automated segmentation and planning in the radiotherapy treatment process, greatly improving overall treatment effectiveness.[A]. There have been numerous reports of AI-based applications in machine and patient-specific QA, including predictions for machine beam data or gamma passing rates on IMRT or VMAT plans. Moreover, the development of these technologies is being pursued for multicenter studies. Radiotherapy must have machine- and patient-specific quality assurance (QA) to ensure safety and accuracy. High-precision radiotherapy, including IMRT and VMAT, has become increasingly difficult to manage on the QA level. This paper will explore the role of Artificial Intelligence in software testing.' Quality Assurance in the new age will be greatly influenced by Artificial Intelligence, as it can significantly reduce time and increase efficiency for developing advanced software. </span></p> |
| title | Application of Artificial Intelligence and Machine Learning in Quality Assurance |
| topic | Artificial Intelligence (AI), Radiotherapy, Quality Assurance (QA), IMRT/VMAT, Automated Segmentation |
| url | https://doi.org/10.5281/zenodo.14950226 |