What stops AI from excelling in healthcare

Every technology has its own set of strengths and challenges. This blog post explores the challenges to AI in healthcare.

AI has rapidly disrupted numerous industries, such as healthcare, retail, manufacturing, and tourism, with its path-breaking innovation. In the last few years, the healthcare industry has been seeing a lot of innovation with repsect to improved treatment, disease analysis, and patient satisfaction. Technology has, to a large extent, changed the way doctors treat their patients. A lot of work has been taking place in the field of AI to pass on its benefits to healthcare. However, along with the benefits, there are also quite a few challenges to AI in healthcare.

What has AI offered the healthcare industry?

Before getting into the challenges being faced by AI in the healthcare industry, let us take a look at some of the sucecssful AI use cases in the sector:

• AI algorithms can analyze the current health status of an individual and predict any sickness that she may suffer in the future. Hence, patients can take preventive measures, which allows them to save their lives and suffering.
• Using deep learning techniques, hospitals can research and publish studies on the causes, symptoms, and effects of diseases as serious as cancer.
• The third use case for AI in the healthcare industry are medical solutions. EMR is an extensively used solution in the healthcare industry. It stores the patient’s clinical data safely and grants immediate access to patient history in case of a medical emergency.
• The fourth AI use case in the healthcare industry is the use of telehealth. Read how Telehealth program is helping the diabetic patients.

What are the challenges to AI in healthcare?

AI algorithms expects a large volume of data to train them to perform better. An AI system is first trained with large swarms of data, or carefully curated data, and lonely then deployed in any application area. If the data that available for training an AI system is inadequate, the system will fail to offer the expected results. Dr, Robert Mittendorff, explains “Curated data sets that are robust and have both the breadth and depth for training in a particular application are essential, but particl hard to access due to privacy concerns, record identification concerns, and HIPAA.”

Another big challenge lies in constructing medical solutions. The expectation is that experts should build AI systems that offer accurate results when implemented in a medical clinic or a hospital. However, doctors who have used AI in their hospitals have a rather disappointing feedback to share. One such feedback comes from Dr. Jose I. Almedia, that goes like, “We implemented our first EMR system hoping it would improve efficiency. We are now on our fourth, and remain disappointed. Right now, it’s been more of a hassle than a time-saver, and has disrupted the doctor/patient relationship by forcing a screen between physicians and their patients.”