AI Set to Transform the Future of Diagnostic

Article By : John Koon

While the drug market may be more lucrative than the diagnostic market, the diagnostic market has an equal or more significant impact...

While the drug market may be more lucrative than the diagnostic market, the diagnostic market has an equal or more significant impact. For example, many more people have received a Covid-19 test than getting medication or in-hospital treatment. In many countries, diagnostic errors affect patient care quality and cause complications and even deaths. Moreover, diagnostic errors often lead to medical compensations to the affected patients, which further adds to the rising cost of healthcare. In addition to rising costs and staff shortage, the volume of medical images is outpacing the availability of specialists who can process them, especially in low- and middle-income countries. The healthcare industry is looking to reduce costs via automating repetitive processes and enabling doctors to make faster, more accurate decisions. As a result, when private companies and public institutions began exploring artificial intelligence (AI) in drug discovery and development, they also tried to apply AI to clinical diagnostics. Watson the pioneer IBM’s AI platform, Watson, was the first platform to make a splash in the diagnostic world. During the 2010s, IBM had formed more than 50 partnerships with prominent institutions such as the U.S. Department of Veteran Affairs, Memorial Sloan Kettering, Johnson & Johnson, and Medtronic to develop AI diagnostic tools to analyze medical images, patient records, or genetic data. Image analysis was viewed as a low-hanging fruit since the researchers can train the AI algorithm with the existing, massive image datasets. However, the hospitals in India, Korea, and the U.S. have found that while Watson was capable of analyzing medical images, its performance and accuracy was uneven with different cancers. Moreover, AI in its current form does not excel in solving complex, nuanced problems with incomplete or disorganized data, such as finding useful information in the research literature or patient records. There was some good news, however. AI was found to excel in more straightforward tasks, such as analyzing genetic data to reach a binary conclusion or performing repetitive tasks such as obtaining basic patient information. A recent paper by researchers at University Hospitals Birmingham NHS has reviewed more than 20,000 research papers on AI’s application in medicine. The study found that high-quality deep learning algorithms could detect various diseases as accurately as health professionals. On the other hand, a pathologist made equally good or even better diagnostic decisions as AI with unlimited time. Since giving pathologists unlimited time to analyze samples is not realistic, many researchers have proposed a hybrid model, where the AI system assist doctors in making effective diagnoses. By 2019, the U.S. FDA has approved more than 30 AI algorithms for healthcare; the applications range from identifying bone fractures in images to detecting diabetic to picking up signs of stroke in CT scans. Potentially, the hybrid model may broaden the application of AI to more diseases and diagnostic functions. Disease detection In the tradition of Watson, AI can be used to detect various diseases. For example, the National Institute of Health, an agency of the U.S. Department of Health and Human Services, has launched an initiative that promotes the use of AI to analyze CT lung scans of Covid-19 patients and improve treatment. Medical imaging is also often used to screen for cancer. For example, determining if a case of breast cancer is malignant or benign is challenging. Misdiagnosis can lead to unnecessary procedures for benign breast cancer patients and missed treatment for malignant patients. AI can help improve the accuracy of image analysis and reduce the number of false positives and false negatives. For example, QuantX is the first FDA-approved AI platform that evaluates breast abnormalities. QuantX quantifies the abnormalities into a single score that helps radiologists reach a diagnosis. AI is also being used to classify different types of skin cancer. Lastly, the Augmented Reality Microscope integrates AI into routine workflows and helps increase the accuracy of detecting metastatic breast cancer and identifying prostate cancer. In addition, AI can be used to analyze chest x-rays and identify heart abnormalities. Also, AI can help automate tasks such as measuring the aortic valve, carina angle, and pulmonary artery diameter. Several related devices have obtained regulatory approval from the U.S. FDA to go to the market. Apple Watch was the first consumer-available product that allows users to take an ECG from their wrist and send it to physicians. Zebra’s Medical Vision platform quantifies the calcium deposit level, which clogs up blood vessels and may cause a stroke or blood clotting in a patient’s coronary artery. Lastly, Bay Labs’ EchoMD AutoEF platform analyzes a patient’s echocardiogram and assesses the level of blood flow and the risk of heart failure. Bone fractures can be hard to detect on standard images. AI may pick up subtle variations in the image that indicate a fracture that requires surgery. This way, AI can help lower the number of false-negative patients and reduce the pain they have to endure. Neurological diseases, such as amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS), often have similar symptoms that are difficult to distinguish. False-positive results are common. AI can help distinguish between ALS and PLS via image analysis. Lastly, Aidoc, an FDA-approved app, allows radiologists to identify acute brain hemorrhages, a life-threatening condition, in head CT scans. Disease prediction AI can also help doctors predict the likelihood of a patient developing certain conditions or screen for the patients who are likely to have a disease. A Swedish University has developed an algorithm that can identify patients at risk of developing septic shock, a life-threatening and often unpredictable condition. Birth asphyxia is hard to predict or prevent. American and Brazilian researchers have developed a fuzzy logic system that analyzes a mother and the baby’s data to predict the likelihood of the baby needing resuscitation so that the doctors can be more prepared. Researchers are also developing an algorithm that predicts patients’ risk of heart attack, breast cancer, or even suicide. Improving the diagnostic process Because AI excels in performing repetitive tasks accurately, it can play a significant role in reducing personnel fatigue and documentation errors. Furthermore, in the current climate of Covid-19, where face-to-face interaction between healthcare workers and patients may increase the chance of infection, AI may serve as a virtual protective gear for the medical staff. For example, chatbots, which are AI robots with speech recognition capability, can gather information from patients, identify patterns in their symptoms, and make diagnosis and treatment recommendations to the doctors. When the symptoms point to common flu, the chatbot can recommend over-the-counter medication; if the symptoms are more serious, the chatbot can direct the patient to a consultation with the doctor. Babylon Health, a startup in the UK, has developed such a chatbot. There are also remote patient monitoring programs. Ada Health, a Berlin-based company, has developed an AI platform to track patient health and offer recommendations based on patient symptoms and other health information. On the other hand, a voice-based virtual nurse program checks on patients between office visits and provides automatic alerts to physicians. This way, virtual nurses can reduce patient anxiety, increase compliance with medication regiments, and maintain patient satisfaction. In addition, AI can synergize with personalized medicine to achieve optimal patient treatment. Due to their different genetic makeup, patients respond to drugs and treatment doses differently. The same dose of a drug will be less effective but safer in patients who metabolize, or break down, the drugs quickly, while it will be more effective but potentially more toxic in patients who metabolize slowly. Therefore, customizing the dosage of a drug for a patient will improve his response to the treatment. Biomarkers are indicator molecules found in fluids, usually blood, in the body. They can help doctors ascertain if a patient has a disease, is at risk of developing a disease, or will respond to a drug. Biomarkers can make disease diagnosis more secure and cheaper, but they are too numerous for humans to consider together. As a result, genetic testing usually focuses on one or a few genes. But with AI, the doctors can consider more genetic variation of a patient and make more accurate decisions on which drug to use and how much. Conclusion The diagnostic field is more risk-averse than drug discovery because diagnostics have direct application to patients. Therefore, the adoption of AI in diagnostics has faced and will continue to face heavy skepticism and concern, as illustrated by the relatively low success rate of AI diagnostic platforms gaining regulatory approval since 2018. One way to alleviate such concern will be to define AI as an assistant role instead of the leading role in diagnostic tests. In addition, the training of the AI model for diagnostics is even more important than drug discovery since diagnostics are directly relevant to patients. Therefore, the sharing of training data will be critical.

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