Unveiling the Potential: Machine Learning Reshaping Nuclear Medicine Diagnostics and Treatment
Keywords:
algorithms, artificial intelligence, Machine Learning, atomic medicine, machine learning, semantic networks, nuclear medicine.Abstract
Embarking on an exploration of the symbiotic relationship between Machine Learning (ML) and the dynamic field of Nuclear Medicine, this comprehensive overview delves into the multifaceted roles and evolving landscape of ML within this specialized domain. The narrative spans from the genesis and progressive trajectory of ML, elucidating the varying algorithms that underpin its functionality, to a discerning delineation of scenarios where each algorithm finds unique relevance and utility in the realm of nuclear medicine. This discourse pivots on an examination of the profound impact ML have already exerted on this domain, elucidating the diverse contributions that have reshaped nuclear medicine while candidly addressing the prospects and limitations that await in the future. It unearths the latent potentials and realistic constraints surrounding the integration of ML, offering a critical evaluation of its current and potential capabilities, paving the way for a holistic understanding of its applications. Amidst the plethora of applications, a focused lens is directed towards the burgeoning studies in low-dose Positron Emission Tomography (PET), disease detection, image reconstruction techniques, and the development of prognostic and outcome prediction models. These advancements, rooted in ML methodologies, mark a pivotal milestone in enhancing diagnostics and prognostics within nuclear medicine, fostering a paradigm shift in patient care and treatment. The culminating section of this discourse sets forth a clarion call to action, advocating for standardized reporting measures in study designs and outcomes. It advocates for a standardized checklist, a guiding beacon for the research community, fostering consistency and coherence in the dissemination of knowledge. Addressing the prevalent issue of variable algorithm presentation in the literature, this segment underscores the pressing need for uniformity and standardized conventions in the publication of ML-driven studies within the domain of nuclear medicine. In essence, this discourse seeks to paint a panoramic view of the vast landscape where ML converges with nuclear medicine, underscoring the need for methodical precision and unified standards in the realm of knowledge dissemination correct.
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