The human resource issue is spreading internationally, and it is apparent that it is not viable to offer care without labor. The healthcare workforce crisis is due to at least three key issues: doctor shortages globally, the aging and burnout of physicians and a greater need for chronic care due to the increase in life expectancy.
An efficient system depends on the availability, accessibility, acceptance, and quality of its health personnel. The human resource issue is escalating worldwide, and it is apparent that without a qualified workforce, quality treatment cannot be provided.
In artificial intelligence (AI), deep learning refers to machine learning that is based on neural networks. Deep learning can perform tasks such as image recognition, natural language processing, and translation. As datasets grow, deep learning algorithms perform better and more efficiently, meaning the larger the dataset, the better it performs and the more efficient it is. Deep learning algorithms have proven to be capable of diagnostic tasks in the past decade.
Artificial intelligence can ease access to health care. Medical professionals can use AI to treat more patients in the allocated time. Moreover, AI solutions can help physicians make better diagnosis and improver treatment results while decreasing medical errors. In addition, AI can be helpful in the medical staff recruitment, selection, and training process. Consequently, it will improve the healthcare workforce shortage.
In the previous decades, skin cancers and melanoma have been on the rise, becoming a very significant public health issue. Among different types of skin cancer, melanoma is one of the deadliest types that includes 80 percent of deaths from skin cancer. Malignant melanoma is one of the clearest cases of a cancer in which early detection is vital to the possibility of effective treatment. The process of diagnosis of dermatological issues can take a lot of time and may require many examinations. Dermatologists need to check the entire body of each patient and record suspicious lesions to make a clear diagnostic decision. On the other hand, this process will cost a lot of money and resources which in some countries patients may not be able to afford.
Since the early detection of skin cancer and melanoma is crucial, iToBoS scanner can be used to accelerate this process using AI solutions, reduce months of examination to possibly hours and decrease the amount of needed healthcare staff. This way, doctors would be able to obtain full body information of patients in less time and with less human error. Plus, the scanner would provide a primary diagnosis on each lesion that could help dermatologists in diagnostic decision making.