Jan 6
2022
Pros and Cons of AI In Healthcare Industry
Across sectors, artificial intelligence (AI) has become commonplace. AI helps streamline tasks, improve efficiency, and simplify complicated procedures in medicine. By 2021, Gartner assumes that 75% of these firms will invest in the potential of healthcare AI to enhance their overall performance.
Even with this, others still debate the pros and cons of the clinical and economic implications of relying on data-driven technology and algorithms for patient care, including:
Pro: It saves resources
As more critical activities are automated, medical practitioners will have more time to examine patients and detect diseases.
With the help of artificial intelligence, medical institutions may save time and money by performing treatments more quickly. Furthermore, AI has the potential to save substantial quantities of cash. It’s estimated that over $200 billion is wasted each year in the healthcare business. Many of these wasted expenses may be traced to tedious administrative tasks such as filing, assessing, and finalizing accounts.
Another area for improvement is determining medical necessity. Traditionally, hours of assessing patient history and information are required to establish medical needs accurately. Natural language processing (NLP) and deep learning (DL) algorithms are developed to help clinicians analyze hospital cases and prevent rejections.
Medical practitioners are given more time to help and interact with patients as resources and critical productivity hours are freed up.
Con: Susceptible to errors
Diagnostic data produced from millions of cataloged examples is the foundation of medical AI. A lack of information on specific environmental factors, illnesses, or demography might be misdiagnosed. Whatever the system, there will always be some missing information. There may be a lack of knowledge about particular populations and treatment responses when writing prescriptions.
This incidence may cause difficulties in identifying and treating individuals from particular backgrounds. To accommodate for data shortfalls, AI is constantly developing and improving. However, it’s vital to highlight that it may still exclude some groups from current domain knowledge.