Machine Learning and Medical Diagnosis: An Introduction to How AI Improves Disease Detection
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Introduction
Today, AI plays an increasing role in patient care. Healthcare providers, researchers and data scientists have teamed up to advance machine learning (ML) and medical diagnoses. Given the aging population and expansion of personalized gene therapies, ML models look likely to play a vital role in future healthcare provision.
Deep machine learning and medical diagnosis examples
Computer vision and ML can enhance the valuable microscope work traditionally carried out by pathologists. Machine vision is a common thread in diagnostic applications, which evaluate physiological data, environmental influences and genetic factors.
Thankfully, a large medical dataset is available to clinicians, ranging from symptoms to test results and image files. Detailed analysis of this resource results in a better understanding of biological mechanisms and risk factors. Next, we consider some examples in detail.
How is machine learning used in medical diagnosis?
Artificial intelligence helps with decision-making, workflow management and the automation of tasks. Healthcare professionals use the latest machine learning in triage to flag abnormalities and prioritize life-threatening cases. Similarly, AI supports physicians in diagnosing cardiac arrhythmia, predicting stroke outcomes and managing chronic diseases.
Deep learning also extends to:
Pathology, to diagnose diseases from laboratory test results.
Dermatology and oncology, to recognize cancerous tissue biopsies.
Diagnosis of genetic disorders and rare diseases based on observed phenotypes.
Facial analysis for measurement of vital signs.
Chat bots that use text or speech recognition to identify patterns in patient symptoms, form provisional diagnoses and recommend treatment or other action(s).
How accurate is AI in medical diagnosis?
AI is already facilitating diagnosis, drug development, personalized treatments and gene editing. Increasingly, these insightful tools are revolutionizing healthcare, thanks to causal reasoning techniques in ML algorithms. Earlier models used only correlations between symptoms and the most likely cause(s).
Writing in Nature Communications, Dr. Jonathan Richens and colleagues outlined the latest approach whereby algorithms work through the possibilities of patients' symptoms being due to various conditions. In research, experts gave AI the ability to abstract alternate realities and consider whether one or more symptoms could be present if the patient had a different disease. These reiterations meant ML algorithms scored higher than seven in ten doctors on written test cases.
Skin cancer detection
Stanford University researchers trained an algorithm to diagnose skin cancer using deep learning. In a convolutional neural network (CNN), an ML algorithm learned to detect cancers and melanoma using large datasets containing 130,000 images of skin lesions and more than 2,000 different diseases.
Physicians diagnose around 5.4 million cases of this type in the US every year. Early detection improves the prognosis for survival but – as statistics show – delays increase mortality rates.
Visual examinations are crucial in dermatology diagnosis. First, a dermatologist inspects the lesion(s) of interest with a dermatoscope, i.e., a handheld microscope. Then, a biopsy is necessary if the tissue appears cancerous or the evaluation is inconclusive.
Researchers tested Stanford's deep learning algorithm against twenty-one certified specialists who examined 370 images. The machine's predictions had the same precision as all the dermatologists in deciding the best course of action*. Nonetheless, the research team highlighted the need for rigorous testing before integrating the ML model into a live environment.
Cellular pathology
Historically, pathologists diagnose certain diseases manually by observing microscope images. This method has changed little in more than a century.
Researchers from Beth Israel Deaconess Medical Center and Harvard Medical School recently labeled hundreds of scan results to speed up diagnoses and boost accuracy. They highlighted cancerous and noncancerous cells to form the training data for deep learning. The ML algorithm achieved 92 percent diagnostic accuracy, marginally lower than the human rate of 96 percent. In combination, however, algorithm predictions and human diagnoses boosted accuracy to 99.5 percent.
Improving rheumatoid arthritis treatment
In the UK, researchers at the Queen Mary University of London used AI to analyze blood samples from rheumatoid arthritis patients and predict their response to treatment. Some anti-rheumatic medications do not benefit as many as half of the patients prescribed them. In contrast, the same drugs could regulate inflammation well in others.
In a Nature Communications article, Professor Jesmond Dalli noted that many patients' conditions had not responded well to their initial prescription. These patients suffered unnecessary side effects for six months while assessing the treatment's effectiveness (or lack thereof) before changing. Their disease had worsened before trying a different treatment that was more likely to work.
Following changes, patients' therapy became more focused and personalized. Doctors used the new biomarkers to foretell the effectiveness of different disease-modifying anti-rheumatic drugs.
Using AI to reduce the risk of heart attacks
A separate UK-based team developed an ML model that spots red flags for coronary incidents. University of Oxford professor Charalambos Antoniades and colleagues discovered a new biomarker called the fat radiomic profile.
The new medical technique flags anomalies in the perivascular space around the heart. In the European Heart Journal, the medics explained how the app identifies genes associated with inflammation, scarring and other changes. The dataset comprised test biopsies from 167 cardiac operations.
According to Professor Antoniades, the absence of noticeable narrowing in a coronary artery does not necessarily exclude the possibility of heart attacks. Therefore, the new diagnosis used AI to search carefully for disease characteristics around blood vessels, thus enabling timely prevention.
Improving patient care
The US Institute of Medicine reported that diagnostic errors contribute to approximately 10 percent of patient deaths and a similar proportion of complications in treatment. Although the performance of medical professionals was not necessarily in question, a significant number of errors arose from communication failure, non-integrated healthcare IT and computers that did not support proper procedure.
As well as addressing the above issues, AI supports clinical staff and helps reduce burnout due to overwork and exhaustion. In addition, the latest healthcare computer systems enable streamlined methods to save time. For instance, radiologists can manage dozens of images with greater ease. As a result, the capacity and accuracy of diagnosis have increased; specialists can quickly check and flag scanner images to deal with urgent cases more efficiently.
Machine learning also benefits drug development and trials, clinical research and robotic surgery. In the latter, automatic suturing could shorten operations and reduce surgeon fatigue.
Of course, continued rigorous testing of these applications will be necessary to validate their utility. At the same time, some clinicians are unconvinced. They envisage challenges in integrating AI in clinical practices without undermining their expertise. Therefore, education and adaptation of healthcare systems will be instrumental in rolling out and adopting these new technologies.
Integrating ML in healthcare
For hospital and clinic managers who want to incorporate ML into their patient care and business administration systems, expert help is at hand. The precise steps vary in line with the tasks under automation, from research and clinical trials to speeding up diagnosis and managing disease. Nonetheless, a common thread is machine model training which involves setting up a training dataset and labeling it – also known as data annotation.
To ensure accurate data annotation, we offer tailored support in implementing its leading-edge healthcare solutions.
Conclusion
AI is, in effect, a second pair of eyes. We have seen an overview of its positive contribution to medical diagnosis, patient treatment and care. Also, ML supports medical practitioners and clinics by alleviating undue workload pressure and maximizing efficiency.
*Journal Nature: Dermatologist-level classification of skin cancer with deep neural networks.
