Hirosawa Takanobu, Suzuki Tomoharu, Shiraishi Tastuya, Hayashi Arisa, Fujii Yoichi, Harada Taku, Shimizu Taro
Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan.
Department of Hospital Medicine, Urasoe General Hospital, Okinawa, Japan.
Int J Gen Med. 2024 Nov 19;17:5417-5422. doi: 10.2147/IJGM.S497753. eCollection 2024.
Artificial intelligence (AI) holds great potential for revolutionizing health care by providing clinicians with data-driven insights that support more accurate and efficient clinical decisions. However, applying AI in clinical settings is often challenging due to the complexity and vastness of medical information. This perspective article explores how AI development methodologies can be adapted to support clinicians in their decision-making processes, emphasizing the importance of a hybrid approach that combines AI capabilities with clinicians' expertise.
We developed a conceptual framework designed to integrate AI-driven hybrid intelligence into clinical practice to enhance decision-making. This framework focuses on adapting key AI concepts, such as backpropagation, quantization, and avoiding overfitting, to help clinicians better interpret complex medical data and improve diagnosis and treatment planning.
Several AI methodologies were adapted to enhance clinical decision-making. First, backpropagation allows clinicians to refine initial assessments by revisiting them as new data emerges, improving diagnostic accuracy over time. Second, quantization helps break down complex medical problems into manageable components, enabling clinicians to prioritize critical elements of care. Finally, avoiding overfitting encourages clinicians to balance rare diagnoses with more common explanations, reducing the risk of diagnostic errors and unnecessary complexity.
The integration of AI-driven hybrid intelligence has the potential to enhance clinical decision-making. By adapting AI methodologies, clinicians can enhance their ability to analyze data, prioritize treatments, and make more accurate diagnoses while preserving the essential human aspect of health care. This framework highlights the importance of combining AI's strengths with clinicians' expertise for more effective and balanced decision-making in clinical practice. This perspective highlights the value of hybrid intelligence in achieving more balanced, effective, and patient-centered decision-making in health care.
人工智能（AI）通过为临床医生提供数据驱动的见解来支持更准确、高效的临床决策，从而在医疗保健领域具有巨大的变革潜力。然而，由于医学信息的复杂性和海量性，在临床环境中应用人工智能往往具有挑战性。这篇观点文章探讨了如何调整人工智能开发方法以支持临床医生的决策过程，强调了将人工智能能力与临床医生专业知识相结合的混合方法的重要性。
我们开发了一个概念框架，旨在将人工智能驱动的混合智能整合到临床实践中以增强决策。该框架专注于调整关键的人工智能概念，如反向传播、量化和避免过拟合，以帮助临床医生更好地解释复杂的医学数据并改进诊断和治疗规划。
几种人工智能方法被调整以增强临床决策。首先，反向传播使临床医生能够在新数据出现时重新审视初始评估，从而随着时间的推移提高诊断准确性。其次，量化有助于将复杂的医学问题分解为可管理的组成部分，使临床医生能够确定护理的关键要素的优先级。最后，避免过拟合鼓励临床医生在罕见诊断与更常见解释之间取得平衡，降低诊断错误和不必要复杂性的风险。
人工智能驱动的混合智能的整合有可能增强临床决策。通过调整人工智能方法，临床医生可以提高他们分析数据、确定治疗优先级和做出更准确诊断的能力，同时保留医疗保健中至关重要的人文因素。这个框架强调了将人工智能的优势与临床医生的专业知识相结合对于在临床实践中做出更有效、更平衡决策的重要性。这一观点突出了混合智能在实现医疗保健中更平衡、有效和以患者为中心的决策方面的价值。
