AI and Healthcare: The Battle Against Misdiagnosis
Medical misdiagnosis is a serious problem across the United States. In fact, studies show that most Americans who go to the doctor will receive a wrong diagnosis at least once in their lives, if not more. Until now, diagnostic errors have been an understudied and often overlooked aspect of healthcare, that can gravely affect patient safety. In fact, this problem is so severe that “medical mistakes are now estimated to kill up to 440,000 people in U.S. hospitals each year, making preventable errors the third leading cause of death in America behind heart disease and cancer.”
As the foundation of potential doctors of the future, artificial intelligence may be positioned to help reduce this statistic. Let’s look deeper into the impacts of medical misdiagnosis, as well as how AI might prevent them.
The Effects of Misdiagnoses
While death is probably the worst consequence of medical misdiagnosis, there are less severe repercussions that warrant attention too. In terms of health, missed, late and/or false diagnoses can lead to life-altering conditions, further worsening many medical conditions and subsequently making them untreatable.
Misdiagnoses can also be a major strain on one’s finances. Today, over 40 million Americans have some sort of medical debt. The expenses associated with misdiagnosis, ineffectual treatment, and subsequent re-diagnoses is no joke. This often leads to a delay in the payment of one’s medical bills, which in turn could result in late fees, interest, and affect one’s credit score. In severe circumstances, as stated by experts at Fiscal Tiger, legal action could ensue: “Your medical provider could sue you for your unpaid medical bills and a court could authorize measures like wage garnishment — where money can be taken straight from your wages to pay your debts — in order to appease them.”
Trending AI Articles:
MS or Startup Job — Which way to go to build a career in Deep Learning?
TOP 100 medium articles related with Artificial Intelligence
Neural networks for algorithmic trading.
Multimodal and multitask deep learningBack-Propagation is very simple. Who made it Complicated ?
How AI Can Help
Improving the accuracy of diagnoses requires significant collaboration amongst all the stakeholders in the medical and healthcare industry. Here is where AI can play a revolutionary role. As stated by WGU in an article on how the landscape of healthcare is changing, “AI is hugely changing how healthcare works, and the potential is even more diverse and amazing.”
Currently, doctors do not get paid to consult with each other. There is no incentive to discuss cases with colleagues. Patients too are often required to pay extra to get a second opinion or a specialist consultation, an expense that goes uncovered depending on one’s insurance plan. One AI application, created by the Human Diagnosis Project (Human Dx), aims to solve this problem.
As stated by the Scientific American, “The goal is to provide timely and affordable specialist advice to general practitioners serving millions of people worldwide, in particular so-called ‘safety net’ hospitals and clinics throughout the U.S. that offer access to care regardless of a patient’s ability to pay.” The app has an interface where physicians can type in a clinical question, their working diagnosis and even upload images and test results to the case they are working on. Then, via the app, the physician is able to request help from specific colleagues or the wider network of doctors who have joined the Human Dx community. Within the next couple of days, the app’s AI consolidates all the responses received into a single report. In this way, the app acts as the equivalent of a “curbside consult” without the hassle of setting up a formal, expensive, external consultation.
Artificial intelligence applications have also been seen to score higher than the average human trainee on exams designed to test diagnostic skills. According to an article on CNBC News, AI from Babylon Health scored 82 percent, as opposed to the human average mark of 72 percent. Ali Parsa, CEO of Babylon Health stated that the results “clearly illustrate how AI-augmented health services can reduce the burden on health care systems around the world. Our mission is to put accessible and affordable health services into the hands of every person on Earth.”
These are just two examples of how AI has proven to be useful in the fight against medical misdiagnosis. Other diagnostic applications of machine learning tend to fall into four categories: chatbots, oncology, pathology, and rare diseases. For instance, in terms of oncology, an article on Emerj states that “Researchers are using deep learning to train algorithms to recognize cancerous tissue at a level comparable to trained physicians.”
Similarly, when it comes to rare diseases, AI combined with facial recognition is being used to help doctors diagnose them. Given its potential applications in genomics, it can also be used to “detect phenotypes that correlate with rare genetic diseases.” Much like in the case of Babylon Health, companies are also using AI chatbots to identify symptoms through patient inputs and form a potential diagnosis. Based on this, some AI can even go so far as to recommend an appropriate course of action.
While some physicians are still dubious that AI can outperform a human in terms of diagnosing, there is no doubt that AI does have substantial potential in diagnostic applications. With the help of artificial intelligence, the medical world will slowly but surely combat the misdiagnoses and problems associated with it.