Artificial intelligence can support doctors by analyzing thousands of medical papers using natural language processing to inform treatment plans. Every technology has its own set of strengths and weaknesses. In the following article, we will explore the challenges of artificial intelligence in the healthcare industry.
Artificial intelligence (AI) has rapidly disrupted numerous industries, such as advertising, retail, manufacturing, and tourism, with its path-breaking applications. In the last few years, the healthcare industry has witnessed several innovations that 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 reducing costs and improving efficiency. Despite promising early signs, there are some serious obstacles that artificial intelligence should overcome to fully enhance the healthcare industry.
Successful Use Cases of Artificial Intelligence in the Healthcare Industry
Before getting into the challenges that are faced by AI in the healthcare industry, let us take a look at some of the most sucecssful use cases in the sector:
- AI algorithms can analyze the current health status of an individual and predict any sickness that he or she may suffer in the future. Hence, patients can take preventive measures, saving their lives and reducing their suffering.
- By using deep learning techniques thanks to AI, hospitals can research and publish studies on the causes, symptoms, and effects of diseases as serious as cancer.
- Artificial intelligence has led to some revolutionary medical solutions. EMR is an extensively used solution in the healthcare industry. It stores patients' clinical data safely, granting immediate access to their history in case of a medical emergency.
- Telehealth programs are helping the diabetic patients. Now that medical telemetry products are wireless and omnipresent, AI software can sift through the mountains of data faster than humans, quickly identifying health problems before they become catastrophic.
Challenges of Artificial Intelligence in the Healthcare Industry
AI algorithms analyse large volume of data learning to perform better. An AI system is first trained with carefully curated data, deployed in any application area. If the data that is 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 as follow, “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.”
In the very complex world of healthcare, artificial intelligence tools can support human providers to provide faster service, diagnose issues and analyze data to identify trends or genetic information that would predispose someone to a particular disease. When saving minutes can mean saving lives, AI can be transformative not only for healthcare but for every single patient.
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