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MISDIAGNOSIS & MEDICAL ERROR 

Misdiagnosis is the incorrect identification of a disease or problem or incorrect data interpretation or incorrect suggestion of treatment procedures that cause Medical errors. The World Health Organization published a document in 2019 named Patient safety Fact File where it is mentioned how  “Inaccurate or delayed diagnosis is one of the most common causes of patient harm and affects millions of patients”.

Medical errors are now the third leading cause of death in the United States of America. Now that is an alarming number! 

“To err is human”, so to minimise misdiagnosis and medical errors we must use technological advancements.

IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE

Artificial Intelligence (AI) is invigorating the modern healthcare system through machines and systems that can predict diseases precisely, comprehend data quickly, learn to adapt and act accordingly.

AI-driven systems are capable of solving misdiagnosis by reducing communication gap problems, human error, by organizing the transfer of knowledge, predicting and identifying inadequate staffing, inadequate information flow and improper health policies.

AI systems are implemented in several health care fields like, 

1. To make intelligent symptom checkers that use defined algorithms to diagnose diseases,
2. In the early detection process of fatal diseases, 
3. To predict the presence of harmful microbes  (bacteria and viruses) in blood samples,
4. In automated image scanning for analysis of clinical reports,
5. To automate repetitive healthcare processes like data migration, various eligibility checks to minimize cost and manpower,
6. To develop interactive and personalized healthcare system,
7. To predict a patient’s life at risk and hospital risk,
8. To develop robotic surgery and heart therapy
9. To develop more effective drugs
10. To develop more structured and informed research and clinical trials.

Above mentioned are just some of the many applications, many more are work in progress as we discuss.

Recently, during the COVID 19 pandemic outbreak, AI became a more imperative tool worldwide as doctors and healthcare professionals, biotechnology companies, research labs and government agencies have started using AI dominant systems for the detection and diagnosis of the virus at various stages and places around the globe.

EXAMPLES

Several companies worldwide have adapted and implemented AI for new inventions and procedures. 

Cleveland Clinic in association with IBM developed personalized health care plans to improve the patient experience. 

Washington based company KenSci partnered with KPMG, Microsoft and GE to develop a risk prediction system using AI and Big Data.

Babylon, a New York-based health service provider developed its mobile application to issue healthcare consultation remotely.

H2O.AI in California, uses AI to collect health data to predict and detect diseases and their severity or stages to improve clinical workflow.

DeepMind health from Google collects a huge data set of comparable symptoms to fabricate an alert system for the doctors and healthcare professionals to help in quicker and more efficient diagnosis.

Biofourmis in Boston created Biovitals, in association with the University of Hong Kong,  a self-monitoring platform for patients in affected regions to predict at home COVID 19 detection and monitoring. 

RELIABILITY

In spite of the pros of AI system implementation and technological inventions, it is still argumentative if the healthcare system can be fully dependent on machine learning and  AI systems and if they can improve or replace the human-driven healthcare system. 

But it has been shown in studies that whereas an AI can detect a disease state with 87% accuracy, healthcare professionals can do the same with 86% accuracy, whereas an AI can signal an all-clear state correctly 93% times, healthcare professionals can 91% times. So we can say that till this time, the efficiency of  AI models in disease state detection is comparable to that of human experts. 

But at the same time if we rely completely on AI-driven systems, which might not be completely devoid of intrinsic errors or probable programming bugs, the tremendous negative impact it might have on the healthcare system worldwide, is incomprehensible.

FUTURE

The future of the modern healthcare system is in the hands of these ubiquitous, machine learnt,  artificially intelligent, cognitive systems. Though AI-driven solution systems have set in motion the massive technological advancement in healthcare, it still has a long list of vital issues to subjugate in order to revolutionize the global healthcare industry.