“[Machine learning] has the potential to transform early cancer detection in primary care […]”
Credit: 2023 Nemlander et al.
“[Machine learning] has the potential to transform early cancer detection in primary care […]”
BUFFALO, NY- July 21, 2023 – A new editorial paper was published in Oncoscience (Volume 10) on June 9, 2023, entitled, “Transforming early cancer detection in primary care: harnessing the power of machine learning.”
Cancer remains a significant global health burden, and early detection plays a crucial role in improving patient outcomes. Primary care settings serve as frontline gatekeepers, providing an opportunity for early detection through symptom assessment and targeted screening. However, detecting early-stage cancer and identifying individuals at high risk can be challenging due to the complexity and subtlety of symptoms.
The challenging nature of early detection is revealed by diagnostic errors in primary care, with cancer being one of the most frequently missed or delayed diagnoses. In recent years, the emergence of machine learning (ML) techniques has shown promise in revolutionizing early detection efforts. In this new editorial, researchers Elinor Nemlander, Marcela Ewing, Axel C. Carlsson, and Andreas Rosenblad from Karolinska Institutet and the Academic Primary Health Care Centre at Region Stockholm explore the potential of ML in enhancing early cancer detection in primary care.
“ML has the potential to transform early cancer detection in primary care by leveraging extensive patient data and improving risk stratification and pre-diagnostic accuracy, hopefully saving lives. However, responsible and equitable implementation of ML models requires careful attention to ethical considerations, collaboration, and validation across diverse populations.”
Continue reading: DOI: https://doi.org/10.18632/oncoscience.578
Correspondence to: Elinor Nemlander
Email: [email protected]
Keywords: Primary care, Early cancer detection, Machine learning, Risk assessment
About Oncoscience:
Oncoscience is a peer-reviewed, open-access, traditional journal covering the rapidly growing field of cancer research, especially emergent topics not currently covered by other journals. This journal has a special mission: Freeing oncology from publication cost. It is free for the readers and the authors.
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Journal
Oncoscience
DOI
10.18632/oncoscience.578
Method of Research
Commentary/editorial
Subject of Research
People
Article Title
Transforming early cancer detection in primary care: harnessing the power of machine learning
Article Publication Date
9-Jun-2023
