Medical imaging plays a vital role in clinical practice. Advancements in machine learning, especially deep learning, have contributed to a better understanding of medical images. Deep learning helps in providing accurate prediction from datasets and can diagnose disease as accuratelyas expert physicians.
In 2017, Artificial Intelligence (AI) scientist Sebastian Thrun along with his colleagues at Stanford University demonstrated that a deep learning algorithm was capable of diagnosing potentially cancerous skin lesions as accurately as a board-certified dermatologist.
Deep learning works through a structure of algorithms termed as Artificial Neural Networks. While the foundations date back to 1950s and 60s, there have been significant developments since 2006. In the past 5-7 years, availability of massive labeled data sets and GPU computing havechanged things and nowdeep learning methods are being used extensively.
Availability of large, high-quality labeled datasets, performing parallel calculation with GPUs, improved architectures with flexibility of the network, and new regularisation techniques played a greater role in the deep learning revolution. Deep learning can help unearth opportunities and patterns in clinical data, enabling care givers offer better patient treatment.
Interestingly, deep learning algorithms become more effective in diagnosis with practice, pretty similar to physicians. Deep neural networks are transforming how physicians diagnose illnesses, making the process faster, cost-effective and more accurate than ever before. Physicians and care-givers can benefit from these by preparing themselves and investing in making their systems advanced and compatible to manage huge computational requirements of deep learning.
From models that can detect suicidal tendencies to ones that can detect tumours and cancerous skin lesions as good as a leading dermatologist, deep learning has taken over diagnostic evaluations.
Deep learning systems in healthcare can derive more value by improving accuracy and developing efficiency in diagnosis and treatment of patient issue. In today’s digital world, human-machine collaboration is key and deep learning will evolve into a stage where they will assist human beings.
Healthcare as a sector has benefited with advancements in technology; AI tools and systems can contribute to effective disease diagnosis andtment. There is huge potential and opportunities galore for deep learning in healthcare but the results depend on how well pain and suffering is reduced while staying focused on improving efficiency & accuracy.
Deep learning applications have evolved over time and are ready to uncover a lot of new possibilities in healthcare. A research report by Frost & Sullivan indicates AI systems will contribute around US$6.7 billion in revenues for global healthcare, a surge of 40 per cent (in CAGR) from US$634 million in 2014. Major players in healthcare have been investing heavily in AI, including acquiring startups focusing on deep learning applications. The road ahead will be exciting but how smooth the path would be will depend on how companies and caregivers gain greater knowledge of the applications and make well-informed decisions.