The Use Of Machine Learning In Medical & Healthcare Practice
As the diagnosis of diseases requires proper treatment planning to ensure the well-being and safety of patients, human error and increased workload can hinder accurate diagnostics. With the advancements in technology, there are unlimited opportunities that are leveraging technology to make healthcare interventions more efficient, precise, and impactful.
Being an essential part of artificial intelligence, Machine Learning has become an incredible innovation in the healthcare industry and medical diagnosis. Let’s have a look at its uses in medical practice.
What Is Machine Learning?
Machine Learning is a subdivision of artificial intelligence that makes use of data, algorithm, and analytics to predict outcomes, thus improving its accuracy without being programmed to do so. [1].
Today in healthcare industry, Machine Learning has become a rapidly evolving field, thus helping countless patients and healthcare professionals.
Uses Of Machine Learning In Healthcare:
- Disease Diagnosis.
Disease diagnosis is the most effective area for Machine Learning to imply. Some genetic diseases and even certain types of cancers cannot be detected with conventional ways. Machine Learning plays a pivotal role early diagnosing life-threatening diseases [2].
- Drug Development.
Drug development is an expensive process because of years of work and millions in investments. Machine Learning has made it possible to identify specific disease targets, discover good candidates, speed up clinical trials, and find biomarkers for disease diagnosis [3].
- Personalized Treatment.
When it comes to treatment plans, such as prescribing medications and managing patients, it can be challenging for healthcare providers. Algorithms used in Machine Learning help doctors in disease management by automating tasks, like scan analysis, executing highly complex operations, and designing the right treatment plan.
- Clinical Trials.
Machine Learning has been reshaping the management of clinical trials by reducing the clinical trials cycle time, personalized patients monitoring, and analyzing over-expanding data [4].
References:
[1] El Naqa I, Murphy MJ. What Is Machine Learning? BT – Machine Learning in Radiation Oncology: Theory and Applications. In: El Naqa I, Li R, Murphy MJ, editors., Cham: Springer International Publishing; 2015, p. 3–11. https://doi.org/10.1007/978-3-319-18305-3_1.
[2] Fatima M, Pasha M. Survey of Machine Learning Algorithms for Disease Diagnostic. J Intell Learn Syst Appl 2017;09:1–16. https://doi.org/10.4236/jilsa.2017.91001.
[3] Réda C, Kaufmann E, Delahaye-Duriez A. Machine learning applications in drug development. Comput Struct Biotechnol J 2020;18:241–52. https://doi.org/https://doi.org/10.1016/j.csbj.2019.12.006.
[4] Toh C. Applications of Machine Learning in Healthcare. In: Kheng JPBE-TY, editor., Rijeka: IntechOpen; 2021, p. Ch. 4. https://doi.org/10.5772/intechopen.92297.
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