Radiotherapy and Oncology, Journal Year: 2025, Volume and Issue: unknown, P. 110955 - 110955
Published: May 1, 2025
Language: Английский
Cancer Discovery, Journal Year: 2024, Volume and Issue: 14(5), P. 711 - 726
Published: March 21, 2024
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of field, with a specific focus on integration. AI applications are structured according cancer type and domain, focusing four most common cancers tasks detection, diagnosis, treatment. These encompass various data modalities, including imaging, genomics, medical records. We conclude summary existing challenges, evolving solutions, potential future directions for field.
Language: Английский
Citations
37
The Lancet, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 1, 2025
Language: Английский
Citations
2
The Innovation Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 100120 - 100120
Published: Jan. 1, 2025
<p>Artificial intelligence (AI) is driving transformative changes in the field of medicine, with its successful application relying on accurate data and rigorous quality standards. By integrating clinical information, pathology, medical imaging, physiological signals, omics data, AI significantly enhances precision research into disease mechanisms patient prognoses. technologies also demonstrate exceptional potential drug development, surgical automation, brain-computer interface (BCI) research. Through simulation biological systems prediction intervention outcomes, enables researchers to rapidly translate innovations practical applications. While challenges such as computational demands, software ethical considerations persist, future remains highly promising. plays a pivotal role addressing societal issues like low birth rates aging populations. can contribute mitigating rate through enhanced ovarian reserve evaluation, menopause forecasting, optimization Assisted Reproductive Technologies (ART), sperm analysis selection, endometrial receptivity fertility remote consultations. In posed by an population, facilitate development dementia models, cognitive health monitoring strategies, early screening systems, AI-driven telemedicine platforms, intelligent smart companion robots, environments for aging-in-place. profoundly shapes medicine.</p>
Language: Английский
Citations
2
Journal of Radiation Research, Journal Year: 2024, Volume and Issue: 65(4), P. 421 - 432
Published: May 27, 2024
Machine- and patient-specific quality assurance (QA) is essential to ensure the safety accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) volumetric modulated arc (VMAT), various recommendations been reported by AAPM Task Groups. With widespread use IMRT VMAT, there an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology quickly growing fields owing advancements computers technology. In treatment process, AI has led development techniques automated segmentation planning, thereby significantly enhancing Many new applications using machine- QA, predicting machine beam data or gamma passing rates VMAT plans. Additionally, these applied technologies are being developed multicenter studies. current review article, application organized future directions discussed. This presents learning process latest knowledge on QA. Moreover, it contributes understanding status discusses
Language: Английский
Citations
10
Japanese Journal of Radiology, Journal Year: 2024, Volume and Issue: 42(10), P. 1100 - 1109
Published: June 10, 2024
Language: Английский
Citations
6
Japanese Journal of Radiology, Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 2, 2024
Abstract Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous ablation, for malignant tumors in a minimally invasive manner. As other medical fields, the application of artificial intelligence (AI) interventional has garnered significant attention. This narrative review describes current state AI applications based on recent literature. A literature search revealed rapid increase number studies relevant to this topic recently. Investigators have attempted use various tasks, automatic segmentation organs, tumors, treatment areas; simulation; improvement intraprocedural image quality; prediction outcomes; detection post-treatment recurrence. Among these, AI-based outcomes been most studied. Various deep conventional machine learning algorithms proposed these tasks. Radiomics often incorporated into models. Current suggests that is potentially useful aspects oncology, from planning follow-up. However, methods discussed are still at research stage, few implemented clinical practice. To achieve widespread adoption technologies procedures, further their reliability utility necessary. Nevertheless, considering progress field, will be integrated practices near future.
Language: Английский
Citations
6
La radiologia medica, Journal Year: 2024, Volume and Issue: 129(9), P. 1275 - 1287
Published: Aug. 3, 2024
Language: Английский
Citations
5
Japanese Journal of Radiology, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 25, 2025
Language: Английский
Citations
0
Clinical and Translational Radiation Oncology, Journal Year: 2025, Volume and Issue: 52, P. 100933 - 100933
Published: Feb. 11, 2025
Highlights•Clinically feasible RT plans by one-click ML-based workflow.•ML-based within investigator-dependant variability.•High potential to increase efficency and accuracy.AbstractIntroductionThe integration of artificial intelligence into radiotherapy planning for prostate cancer has demonstrated promise in enhancing efficiency consistency. In this study, we assess the clinical feasibility a fully automated machine learning (ML)-based "one-click" workflow that combines segmentation treatment planning. The proposed was designed create clinically acceptable plan inter-observer variation conventional plans.MethodsWe evaluated fully-automated on five low-risk patients treated with external beam compared results optimized inverse planned based contours six different experienced radiation oncologists. Both qualitative quantitative metrics were analyzed. Additionally, dose distribution segmentations (manual vs. manual automation).ResultsThe automatic deep-learning target volume revealed close agreement between expert referring Dice Similarity- Hausdorff index. However, CTVs had significantly smaller than (47.1 cm3 62.6 cm3). provide coverage range variability observed plans. Due CTV PTV (expert contours) lower plans.ConclusionOur study indicates tested is leads comparable This represents promising step towards efficient standardized treatment. Nevertheless, cohort, auto associated volumes contours, highlighting necessity improving models prospective testing automation therapy.
Language: Английский
Citations
0
Medical Visualization, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 16, 2025
The fusion of artificial intelligence with medical imaging is undoubtedly a progressive innovative process in the modern development domestic healthcare, which allows for unprecedented accuracy and efficiency diagnosis planning special treatment various diseases, including malignant tumors. At same time, approaches, especially field clinical application radiotherapy techniques, are spreading more widely moving from specialized research to already accepted traditional practice. Purpose study: analyze approaches techniques antitumor Conclusion. further provides provision options prevention, cancer patients against background constant increase their implementation, assistance optimizing radiotherapeutic neoplasms.
Language: Английский
Citations
0