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Artificial intelligence (AI) is transforming oncology by enhancing diagnostic precision and optimizing treatment strategies. Traditional cancer diagnostics often face challenges such as interobserver variability and delayed treatment initiation. AI offers a promising solution by integrating medical imaging, genomic data, and clinical records to improve accuracy, reduce delays, and enhance clinician confidence. However, concerns related to ethical implementation, algorithmic biases, and clinical acceptance remain. This study evaluates the impact of AI-assisted oncology workflows on diagnostic accuracy, time to treatment initiation, and clinician satisfaction compared to traditional methods. A quasi-experimental study was conducted at a tertiary cancer center involving 500 newly diagnosed cancer patients. Participants were divided into two groups: an AI-assisted diagnostic and treatment planning group and a control group using conventional methods. AI tools were used for image analysis, genetic data interpretation, and treatment recommendations, with clinicians validating AI-generated insights. Primary outcomes included diagnostic accuracy, time to treatment initiation, and clinician satisfaction. Statistical analyses, including multivariate regression, assessed AI’s impact on these parameters. AI-assisted workflows demonstrated a significant improvement in diagnostic accuracy (96% vs. 89%, p < 0.001), a reduction in median time to treatment initiation (12 vs. 22 days, p < 0.01), and higher clinician satisfaction scores (4.5/5 vs. 3.8/5, p < 0.001). Multivariate analysis confirmed AI as an independent factor enhancing diagnostic precision and reducing delays (aOR 2.8, p < 0.001). AI integration in oncology significantly enhances diagnostic accuracy, expedites treatment initiation, and improves clinician satisfaction. Despite its transformative potential, challenges such as ethical concerns, algorithmic transparency, and clinical adaptability must be addressed. Future research should focus on long-term patient outcomes, cost-effectiveness, and equitable AI implementation to optimize its role in cancer care.

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Transforming Oncology Care: The Role of Artificial Intelligence in Improving Diagnostic Accuracy and Treatment Decisions

  • 章节

  • 发表日期：

    2025年

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  • Mangesh Jabade,
  • Jasneet Kaur,
  • Sheela Upendra,
  • Ranjit Kumar

摘要

Artificial intelligence (AI) is transforming oncology by enhancing diagnostic precision and optimizing treatment strategies. Traditional cancer diagnostics often face challenges such as interobserver variability and delayed treatment initiation. AI offers a promising solution by integrating medical imaging, genomic data, and clinical records to improve accuracy, reduce delays, and enhance clinician confidence. However, concerns related to ethical implementation, algorithmic biases, and clinical acceptance remain. This study evaluates the impact of AI-assisted oncology workflows on diagnostic accuracy, time to treatment initiation, and clinician satisfaction compared to traditional methods. A quasi-experimental study was conducted at a tertiary cancer center involving 500 newly diagnosed cancer patients. Participants were divided into two groups: an AI-assisted diagnostic and treatment planning group and a control group using conventional methods. AI tools were used for image analysis, genetic data interpretation, and treatment recommendations, with clinicians validating AI-generated insights. Primary outcomes included diagnostic accuracy, time to treatment initiation, and clinician satisfaction. Statistical analyses, including multivariate regression, assessed AI’s impact on these parameters. AI-assisted workflows demonstrated a significant improvement in diagnostic accuracy (96% vs. 89%, p < 0.001), a reduction in median time to treatment initiation (12 vs. 22 days, p < 0.01), and higher clinician satisfaction scores (4.5/5 vs. 3.8/5, p < 0.001). Multivariate analysis confirmed AI as an independent factor enhancing diagnostic precision and reducing delays (aOR 2.8, p < 0.001). AI integration in oncology significantly enhances diagnostic accuracy, expedites treatment initiation, and improves clinician satisfaction. Despite its transformative potential, challenges such as ethical concerns, algorithmic transparency, and clinical adaptability must be addressed. Future research should focus on long-term patient outcomes, cost-effectiveness, and equitable AI implementation to optimize its role in cancer care.