Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search t...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/7/3223 |
| _version_ | 1797440627041894400 |
|---|---|
| author | Miriam Santoro Silvia Strolin Giulia Paolani Giuseppe Della Gala Alessandro Bartoloni Cinzia Giacometti Ilario Ammendolia Alessio Giuseppe Morganti Lidia Strigari |
| author_facet | Miriam Santoro Silvia Strolin Giulia Paolani Giuseppe Della Gala Alessandro Bartoloni Cinzia Giacometti Ilario Ammendolia Alessio Giuseppe Morganti Lidia Strigari |
| author_sort | Miriam Santoro |
| collection | DOAJ |
| description | In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers. |
| first_indexed | 2024-03-09T12:10:57Z |
| format | Article |
| id | doaj.art-a7f89ce32d33447b9d52b77e784b327a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:10:57Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a7f89ce32d33447b9d52b77e784b327a2023-11-30T22:52:43ZengMDPI AGApplied Sciences2076-34172022-03-01127322310.3390/app12073223Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and BeyondMiriam Santoro0Silvia Strolin1Giulia Paolani2Giuseppe Della Gala3Alessandro Bartoloni4Cinzia Giacometti5Ilario Ammendolia6Alessio Giuseppe Morganti7Lidia Strigari8Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, ItalyDepartment of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, ItalyDepartment of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, ItalyDepartment of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, ItalyIstituto Nazionale di Fisica Nucleare (INFN) Sezione di Roma 1, 00185 Roma, ItalyDepartment of Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, ItalyDepartment of Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, ItalyDepartment of Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, ItalyDepartment of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, ItalyIn recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.https://www.mdpi.com/2076-3417/12/7/3223artificial intelligenceradiotherapyworkflowmachine learningdeep learningiterative optimization |
| spellingShingle | Miriam Santoro Silvia Strolin Giulia Paolani Giuseppe Della Gala Alessandro Bartoloni Cinzia Giacometti Ilario Ammendolia Alessio Giuseppe Morganti Lidia Strigari Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond Applied Sciences artificial intelligence radiotherapy workflow machine learning deep learning iterative optimization |
| title | Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond |
| title_full | Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond |
| title_fullStr | Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond |
| title_full_unstemmed | Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond |
| title_short | Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond |
| title_sort | recent applications of artificial intelligence in radiotherapy where we are and beyond |
| topic | artificial intelligence radiotherapy workflow machine learning deep learning iterative optimization |
| url | https://www.mdpi.com/2076-3417/12/7/3223 |
| work_keys_str_mv | AT miriamsantoro recentapplicationsofartificialintelligenceinradiotherapywhereweareandbeyond AT silviastrolin recentapplicationsofartificialintelligenceinradiotherapywhereweareandbeyond AT giuliapaolani recentapplicationsofartificialintelligenceinradiotherapywhereweareandbeyond AT giuseppedellagala recentapplicationsofartificialintelligenceinradiotherapywhereweareandbeyond AT alessandrobartoloni recentapplicationsofartificialintelligenceinradiotherapywhereweareandbeyond AT cinziagiacometti recentapplicationsofartificialintelligenceinradiotherapywhereweareandbeyond AT ilarioammendolia recentapplicationsofartificialintelligenceinradiotherapywhereweareandbeyond AT alessiogiuseppemorganti recentapplicationsofartificialintelligenceinradiotherapywhereweareandbeyond AT lidiastrigari recentapplicationsofartificialintelligenceinradiotherapywhereweareandbeyond |