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Advancements in artificial intelligence over the last several years are revolutionizing the healthcare landscape as we know it. The potential for precise diagnoses and predictions with a near-zero margin of error has drawn a considerable amount of attention to the application of machine learning in nearly every facet of healthcare. When implemented alongside natural language processing and deep learning, AI and machine learning algorithms can analyze data patterns to effectively diagnose patients and identify potential treatments and solutions with a higher degree of accuracy.
In the past, intricacy has proven to be the biggest obstacle when it came to the widespread application of machine learning in healthcare, as it was notoriously difficult to build and implement a machine learning model. In order to create a model, developers needed to begin by collecting and preparing the training data, an arduous process which could prove extremely time-consuming in and of itself. Next, they needed to choose the right algorithm, establish the framework, train the model, tune the parameters, deploy the model, and analyze its overall performance.
This entire process then needed to be repeated perpetually in order to verify that the model was continuing to perform as expected over a period of time. All of this requires a considerable amount of computing power, storage, expertise, and time; and the complexity of this process meant that building machine learning models was generally inaccessible to all but the most proficient developers. This would all change, however, with the introduction of Amazon SageMaker.
Amazon SageMaker is a fully managed service that eliminates challenges at every step of the process, making it substantially easier and more efficient for data scientists and developers to create, train and deploy machine learning models. In addition to simplifying the process, this innovative service also offers a plethora of unique features and capabilities that allow developers to customize and scale their model with ease.
Distributed Training Libraries enable training for larger datasets and models, and Managed Spot Training can reduce the overall training cost by 90%. Features such as SageMaker Clarify, Autopilot, and One-Touch Deployment are all designed to eliminate nearly every hurdle that made machine learning models all but inaccessible in the past. SageMaker also offers dozens of optimized algorithms to choose from, many of which are designed around specific functionalities, but you can also elect to utilize your own algorithm if you so prefer.
In addition, Sagemaker is interoperable with most other AWS services. This allows users to attain efficient data insight and detailed performance monitoring, while integration with database, data flow and big data analysis enables users to automatically generate reports in order to meet compliance requirements.
Amazon SageMaker is facilitating the development and implementation of machine learning within nearly every modern industry, but perhaps its single most promising and impactful application is within the field of medicine. SageMaker naturally takes audit compliance, data security, and version control into consideration, making it ideal for healthcare and life sciences organizations. From streamlining processes, to personalizing treatments, to maintaining patient privacy, artificial intelligence has the ability to fundamentally transform our healthcare system in ways that benefit patients, providers and institutions alike.
Most recently, healthcare organizations have been building and deploying machine learning models via SageMaker to automatically detect anomalies (such as metastasis) in medical imaging, specifically with regards to the fields of radiology, pathology and dermatology. Once identified, these images are flagged for deep analysis, which ultimately accelerates and improves the overall accuracy of the patient diagnosis. SageMaker provides an extensive array of built-in algorithms that are optimized for computer vision and image classification in order to reduce subjectivity in diagnosis while simultaneously reducing the workload of pathologists.
Artificial intelligence continues to pave the way for entirely new possibilities, and services like Amazon SageMaker are providing people with the tools they need to design, build and deploy machine learning applications that can change the world for the better. By empowering developers to create machine learning models that can assist in an analytical capacity within the medical field, SageMaker is enabling providers to devote more time and resources to direct care and treatment, thus improving the patient experience and potentially saving lives at a substantially lower cost to healthcare institutions.
