Impact of Deep Learning Application in Healthcare Decision Making

Deep learning technology’s application has several positive impacts on the healthcare decision-making process.  First, deep learning technology’s application eases and increases the accuracy of healthcare decision making processes. Deep learning will involve the use of the machine, which solves complex problems using diverse, interconnected, and unstructured data (Shahid et al., 2019). Such kind of data challenges the healthcare providers at solving any presenting problems, and will possibly either makes an error or take a long time. The prevalence of errors compromises the quality of clinical decisions, thus making the associated manual decision-making process substandard, unlike one that applies deep learning technology. Deep Learning Technology Application in Healthcare Essay

Healthcare decision-making processes involving deep learning technology are fast with minimal errors, even where there are diverse and unstructured data, which increases the accuracy of the entire process and resultant decisions.  More so, deep learning technology’s application reduces misdiagnosis instances. For instance, a recent relative study reveals that the use of deep learning technology, decreasing breast cancer misdiagnosis by 85 %( Kontzer, 2018). Thus, the use of deep learning technology eases, reduces errors, and increases the accuracy of healthcare decision-making processes. Deep Learning Technology Application in Healthcare Essay

More so, deep learning technology application increases the effectiveness of healthcare decision making processes and relative decisions. The application of deep learning technology will enable physicians and clinicians diagnoses and distinguish specific cancer a patient is suffering from and at an early age, unlike the previous mechanisms. For instance, the application of LYmph Node Assistant, a Google-associated deep learning algorithm, has a 99% success rate in the cancerous therapy decision compared to human doctors with 38% or previous mechanisms (Liu et al., 2019). The application LYmph Node Assistant detects 99% of the metastasized breast cancer cases using the pathology images, both accurately and fast- concepts symbolizing its effectiveness. Most of the previous cancer mechanisms, such as magnetic resonance imaging and computed tomography, are unable to detect a large number of cancers at their early stage. Therefore, the application of deep learning technology in healthcare decision-making processes is beneficial for it results in an outstanding decision such as the cancerous therapy ones. Deep Learning Technology Application in Healthcare Essay