Artificial intelligence has found its way into the healthcare sector in different dynamics. Deep learning plays a pivotal role in the diagnostic. Researchers are exploring ways to make it efficient enough to be used as part of standard practice.


This tech is developing solutions for the healthcare domain, especially in diagnosis. It helps physicians or doctors prioritize their time more efficiently and reduce their wait times for patient consultations using chatbots for this purpose.


The impact of AI on mental health is enormous, with chatbots that remind patients to take medication or therapists to predict their patient’s moods based on their speech patterns.


In healthcare operational cost management, machine learning algorithms predict healthcare supplies or equipment demand. AI systems can monitor the physiological data of patients, especially in ICU and delivery rooms, for early prediction of complications. Using data AI can quickly help reduce operational costs with proper supply-demand management.


It has helped the medical research community to find solutions for chronic diseases, including diabetes, cancer, and heart disease. Deep learning is used in radiography to detect pneumonia in the lungs. Using neural networks, AI can diagnose diseases using retina scans as its input parameter based on if they have diabetes or not. AI does even treatment of cancer with a high accuracy rate.


Clinical decision-making is another area where AI can help doctors with its capabilities. IBM Watson for Oncology is an AI system designed to give treatment options after the doctors enter the patient’s data. This helps create personalized medicine or treatment plans for patients who have cancer.


AI has also started reducing human errors in medication dispensation at hospitals by suggesting the correct dose based on weight, age, and other relevant parameters. It also monitors the patient’s health condition during medication for any adverse effects or side effects, which can be avoided by early detection.


AI’s various roles in the healthcare sector cannot be overlooked. The list of positive impacts is much longer, but the above points are enough to emphasize how important it is.


However, despite all these benefits, there are some challenges for AI in healthcare. The first one is the unavailability of a massive amount of data required for deep learning systems because most healthcare information comes from private clinics that are reluctant to share their data for research purposes.