Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment

By H. Rajula, G. Verlato, M. Manchia, Nadia Antonucci, V. Fanos. Medicina, 2020

530 citations recorded by the source corpus.

Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The benefits of ML comprise flexibility and scalability compared with conventional statistical approaches, which makes it deployable for several tasks, such as diagnosis and classification, and survival predictions. However, much of ML-based analysis remains scattered, lacking a cohesive structure. There is a need to evaluate and compare the performance of well-developed conventional statistical methods and ML on patient outcomes, such as survival, response to treatment, and patient-reported outcomes (PROs). In this article, we compare the usefulness and limitations of traditional statistical methods and ML, when applied to the medical field. Traditional statistical methods seem to be more useful when the number of cases largely exceeds the number of variables under study and a priori knowledge on the topic under study is substantial such as in public health. ML could be more suited in highly innovative fields with a huge bulk of data, such as omics, radiodiagnostics, drug development, and personalized treatment. Integration of the two approaches should be preferred over a unidirectional choice of either approach.

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

View Paper: https://www.semanticscholar.org/paper/fdfbab0ef403db3bbecacd70c356a927f9165ddd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. J. Onofrey
2. James L. Cross
3. Michael A. Choma

In Health Informatics

1. J. Onofrey
2. James L. Cross
3. Michael A. Choma

Citations in the corpus

• Bias in artificial intelligence algorithms and recommendations for mitigation (2023, background): “AI-based algorithms tend to incorporate a variety of conventional statistical methods and intensive computational machine learning methods to help understand patterns and associations within high-dimensional, nonlinear, and multimodal data [8].”
• Bias in medical AI: Implications for clinical decision-making (2024, background): “In the healthcare domain, medical AI and ML models have demonstrated the potential to improve patient outcomes, reduce cost burdens, accelerate clinical trials, and revolutionize the way we diagnose, treat, and prevent disease [26,28–31].”