understanding to support evidence-based decision-making, particularly for complex diseases such as cancer. These systems leverage NLP capabilities to extract relevant information from vast medical literature, clinical trials, and patient histories, subsequently assisting clinicians in formulating precise treatment plans that align with the latest guidelines (vgl. Adler-Milstein et al. 2022). For example, IBM Watson enables the development of personalized oncology treatment strategies by integrating patient-specific data with international clinical standards (vgl. Guan 2019). However, despite their significant potential, such platforms face criticism over operational and financial barriers. The high costs associated with implementing and maintaining systems like IBM Watson can limit their accessibility, particularly in resource-constrained healthcare environments (vgl. Howard/McGinnis 2022). Moreover, compatibility issues with existing infrastructure, such as EHR interoperability challenges, continue to hinder seamless adoption, highlighting the need for better standardization and technical solutions to bridge such gaps. Another critical concern associated with NLP systems is the ethical challenge of algorithmic bias, which can result from limitations in the training datasets. Language models powering NLP systems often reflect the socioeconomic, cultural, or regional biases embedded in their training data. If minority groups or underrepresented clinical scenarios are inadequately represented, the diagnostic recommendations generated by these algorithms may be less accurate for these populations, further exacerbating existing disparities in healthcare access and quality (vgl. Kumari et al. 2025). For example, the failure to include diverse linguistic and cultural variations in the training process of NLP systems can negatively affect the accuracy of diagnostic outcomes for non-majority populations (vgl. Guan 2019). To mitigate such risks, efforts must focus on diversifying training datasets to ensure that models adequately represent a wide range of demographics, clinical conditions, and healthcare contexts. Additionally, robust frameworks should be established to ensure that NLP systems consistently prioritize fairness and transparency in their application (vgl. Adler-Milstein et al. 2022). NLP technologies have also contributed significantly to improving clinical workflow efficiency, particularly through the development of voice-recognition and transcription systems. These systems allow for the automatic transcription of physician-patient interactions into structured clinical notes, reducing the administrative workload for healthcare providers and ensuring consistent documentation (vgl. Adler-Milstein et al. 2022). This functionality not only enhances productivity but also helps mitigate clinician burnout, enabling medical professionals to dedicate more time to patient-centered care. However, the widespre
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