Integrating artificial intelligence (AI) and robotics in prostate cancer (PCa) offers a game-changing breakthrough with far-reaching implications for diagnosis, treatment, and research. AI-driven algorithms have tremendous promise for assisting early diagnosis by analyzing invisible trends within medical imaging devices such as MRI and ultrasounds. In addition, by evaluating big datasets containing patient data, genetic attributes, and treatment outcomes, these AI algorithms offer the possibility of allowing individualized treatment regimens. This ability to personalize actions to specific patients might improve therapy efficacy while reducing side effects. Robotics can increase accuracy in less invasive surgery, revolutionize therapies like prostatectomies, and improve recovery time for patients. Robotic-assisted procedures provide clinicians with remarkable skills and flexibility, allowing clinicians to negotiate complicated anatomical structures more precisely. However, the symbiotic combination of AI and robotics has several drawbacks. Concerns about data privacy, algorithm biases, and the need to continually assess AI's diagnostic proficiency offer significant hurdles. To ensure patient privacy and data security, the ethical and regulatory aspects of integrating AI and robotics require proper attention. However, combining AI and robotics opens up a galaxy of possibilities. The joint use of AI and robotics can potentially speed up drug development procedures by filtering through massive databases, resulting in the identification of new medicinal compounds. Furthermore, combining AI and robotics might usher in an innovative era of personalized medicine, allowing healthcare providers to design therapies based on detailed patient profiles. The merging of AI and robotics in PCa care gives up unprecedented prospects. While limitations highlight the necessity for caution, the possibilities of better diagnostics, tailored therapies, and new research pathways highlight the transformational abilities of AI and robotics in determining the future of PCa management. This study explores the limitations and opportunities presented by using AI and robotics in the context of PCa.
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http://dx.doi.org/10.7759/cureus.46021 | DOI Listing |
Arthroplast Today
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Südtiroler Sanitätsbetrieb, Department Orthopaedic Surgery, Brixen, Italy.
Background: Unrestricted kinematic alignment (uKA) in total knee arthroplasty (TKA) has the theoretical advantage of reproducing patients' constitutional alignment and restoring the pre-arthritic joint line position and obliquity. However, modifications of the original uKA technique have been proposed due to the potential risk of mechanical failure and instability. Given the significant variability in soft tissue behavior within the same bony morphology group, uKA pure knee resurfacing could be occasionally detrimental.
View Article and Find Full Text PDFFront Plant Sci
December 2024
Department of Agricultural Engineering, College of Engineering, China Agricultural University, Beijing, China.
Front Robot AI
December 2024
Graduate School of Human Development and Environment, Kobe University, Kobe, Japan.
This study investigated whether a singer's coordination patterns differ when singing with an unseen human partner versus an unseen artificial partner (VOCALOID 6 voice synthesis software). We used cross-correlation analysis to compare the correlation of the amplitude envelope time series between the partner's and the participant's singing voices. We also conducted a Granger causality test to determine whether the past amplitude envelope of the partner helps predict the future amplitude envelope of the participants, or if the reverse is true.
View Article and Find Full Text PDFBiomater Res
December 2024
Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Republic of Korea.
[This corrects the article DOI: 10.34133/bmr.0012.
View Article and Find Full Text PDFLancet Reg Health Am
January 2025
Neurology and Neurosurgery Department Hospital Moinhos de Vento, Porto Alegre, RS, Brazil.
Background: Current literature highlights a gap in precise stroke cost data for Latin America. This study measures the real costs associated with acute ischemic stroke care in Latin America using Time-Driven Activity-Based Costing (TDABC). The findings aim to lay a solid foundation for adopting value-based healthcare (VBHC) strategies in the region.
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