Abstract
Machine learning (ML) applications are used in variety of real-world situations, and they are proving a big impact on the healthcare sector. It has become a reality in clinical practice. ML algorithms are applied to examine disease predictions and medical records. The healthcare data can be utilized to find the best trial sample, acquire additional data points, evaluate ongoing data from study participants, and avoid data-based errors. Many recent efforts have aided the adoption of machine learning techniques in the medical sector to allow healthcare providers to focus on patient care rather than searching information. Most of the existing ML techniques have external and internal drawbacks that prevent their ultimate implementation in the clinical domain and various concepts related to ML need to be implemented in the medical studies so that health care practitioners can effectively interpret and guide research in this field. For this reasons, this paper analyzes the present state of ML, as applied to healthcare in a strengths, weaknesses, opportunities and threats (SWOT) analysis.
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Sabouri, Z., Gherabi, N., Massari, H.E., Mhamedi, S., Amnai, M. (2023). A SWOT Analysis for Healthcare Using Machine Learning. In: Farhaoui, Y., Rocha, A., Brahmia, Z., Bhushab, B. (eds) Artificial Intelligence and Smart Environment. ICAISE 2022. Lecture Notes in Networks and Systems, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-031-26254-8_19
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