Machine Learning in Pediatrics

Many industries are being disrupted by the introduction of Artificial Intelligence (AI) products like ChatGPT and Bard, including the medical industry. AI is also being employed more and more in pediatric care in a variety of subspecialties. But there are still a lot of significant obstacles in the way of using AI practically. As such, a succinct synopsis of the functions of AI in the several pediatric medical disciplines that this study aims to cover is necessary.

In pediatric medicine, artificial intelligence (AI) is proving to be disruptive. It is now linked to opportunities, problems, and the requirement for explainability. Artificial intelligence (AI) ought to be seen as an adjunct to, not as a replacement for, human judgment and skill in clinical decision-making. Thus, in order to guarantee the generalizability of research findings, future investigations should concentrate on gathering extensive data.

Increase in ML in medical settings

As technology advances, machine learning offers an exciting potential in the field of healthcare to enhance the precision of diagnoses, personalize care, and discover fresh approaches to problems that have persisted for decades. A tangible influence on the health of your community can result from using machine learning to program computers to make connections and predictions and to unearth crucial insights from vast volumes of data that healthcare providers might otherwise overlook.

Machine learning seeks to enhance patient outcomes and generate previously unobtainable medical insights. Through predictive algorithms, it offers a means of validating the judgement and judgements of physicians. Consider a scenario where a patient is given a specific drug by a doctor. Then, using data from a patient who received the same treatment and benefited from it, machine learning can verify this treatment plan.

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