Deep Learning in Medical Diagnosis: How AI Saves Lives and Cuts Treatment Costs

“Symptoms never lie,” said Dr. House, the most brilliant diagnostician of all time, who, alas, existed on TV screens only. In real life, symptoms are often tricky to spot even by the best experts, while diagnostic mistakes are acknowledged as the most frequent and harmful medical errors, with between 12 to 18 million Americans facing some type of misdiagnosis each year.

There is hope that artificial intelligence (AI) and machine learning (ML) can change this unsettling situation for the better. This article highlights the most successful examples of machine learning applications in diagnosis, accentuates its potential, and outlines current limitations.

AI in disease detection: the current state of things

Four years later, deep learning remains the most promising and widely used ML technique for radiology in particular and disease detection in general. It comes as no surprise as diagnostic imaging prevails in clinical diagnosis and image recognition is a natural fit for deep learning algorithms. That’s what they can do best.

However, it’s no less obvious that machines still can’t replace live experts. “What you see is that [deep learning] is used to support the doctors or do a pre-selection and prioritize cases if there are many patients in the queue,” describes the actual situation Erwin Bretscher, a healthcare consultant at Conclusion, who among other things, advises businesses on artificial intelligence.

There are several drivers that push forward the use of deep learning in radiology and other diagnostic practices:

  • the continued growth of computing power and storage technologies,
  • declining cost of hardware,
  • rising cost of healthcare,
  • the shortage of healthcare workers, and
  • an abundance of medical data to train models. In the US alone, 60 billion radiology images are generated annually — not to mention other data.

Today, most deep learning algorithms augment the diagnostic workflow, but by no means replace human specialists. Below, we’ll explore the most promising use cases of AI in healthcare and give examples of ML-driven solutions, commercially available in North America (FDA-cleared), Europe (CE-marked), or both.

Breast cancer screening

The procedures vary from country to country. For instance, American women go for a mammogram (X-ray of the breast) every one to two years and each image is analyzed by a single radiologist. British women are screened once every three years but with two experts providing results. Though neither approach is perfect, double reading shows better accuracy.

AI advancement and promised benefits

Another recent research run by Korean academic hospitals revealed that AI had higher sensitivity in detecting cancer compared to human experts — especially, when dealing with fatty breasts (90 vs 78 percent).

The studies are still in their early stages with more clinical trials needed. For now, models can serve as an additional reader to automatically produce the second opinion. Potentially, they will plug a growing shortage of trained radiologists.

Commercially available solutions

Transpara by ScreenPoint Medical (based in the Netherlands, FDA-cleared, CE-marked). Trained on over a million mammograms, Transpara deep learning algorithm helps radiologists analyze both 2D and 3D mammograms. The solution is already in use in 15 countries, including the USA, France, and Turkey.

Early melanoma detection

AI advancement and promised benefits

That’s how a CNN developed at Stanford classifies skin lesions from images. Source: ExtremeTech

A year later the European Society for Medical Oncology (ESMO) showed even better results: The CNN correctly detected melanomas in 95 percent of cases while the accuracy of dermatologists was 86.6 percent.

Finally, in March 2020, the Journal of Investigative Dermatology published the study by researchers from Seoul National University. Their CNN model learned from over 220,000 images to predict malignancy and classify 134 skin disorders. Again, AI proved its capability to distinguish between melanoma and birthmarks at human expert level.

Besides enhancing the speed and accuracy of diagnosis, there are plans to run CNN algorithms on smartphones for non-professional skin exams. This can encourage people to visit dermatologists for lesions that might be ignored otherwise.

Commercially available solutions

SkinVision (based in the Netherlands, CE-marked). The app is designed for assessing the risk of cancer based on photos of suspicious moles or other marks. Its AI algorithm was trained to spot warning signs on 3.5 million pictures. SkinVision has already contributed to the diagnosing of 40,000 cases of skin cancer. The app is available for iOS and Android worldwide, except for the US and Canada. However, it by no means can be a substitute for a visit to a dermatologist.

skinScan by TeleSkin ApS (based in Denmark, CE-marked). The iOS app available for downloading in Scandinavia, New Zealand, and Australia, uses an AI algorithm for distinguishing a typical mole from an atypical one.

Lung cancer screening

AI advancement and promised benefits

The odds are that before long AI systems will assist radiologists in analyzing large numbers of CT images, thus contributing to successful treatment and increasing survival rate.

Commercially available solutions

Veye Chest analyzes nodules using AI.

ClariCT.AI by ClariPi (based in South Korea, FDA-cleared). This solution doesn’t detect cancer, but denoises low-dose and ultra-low-dose CT scans, thus improving the confidence of radiologists. The CNN model was trained on over a million images of different parts of the body, but ClariPi accentuates lung cancer screening as a key application of their algorithm.

Diabetic retinopathy screening

AI advancement and promised benefits

This result was outperformed by Google. In collaboration with its sister organization, Verily, the tech giant had been training a deep neural network for three years, using a dataset of 128,000 retinal images. In 2018, Google’s AI Eye Doctor demonstrated 98.6 percent accuracy, on par with human experts. Now the algorithm serves to help doctors at Aravind Eye Hospital in India.

Five levels of DR severity detected on retinal images. Source: Adafruit

In view of the growing number of people with diabetes, AI-fueled screening systems may reduce the burden on eye technicians and ophthalmologists. Early detection also means a cheaper treatment: the drug cost for severe pathology may increase more than tenfold compared with early phase treatment.

Commercially available solutions

1) visit an ophthalmologist (for more than mild DR spotted) or

2) rescreen in 12 months (for mild and negative results).

IRIS (based in Florida, USA, FDA-cleared). Intelligent Retinal Imaging Systems can work with different cameras as it automatically enhances the quality of original images. The company benefits from Microsoft’s Azure Machine Learning Package for Computer Vision.

Cardiac risk assessment from electrocardiograms (ECGs)

AI advancement and promised benefits

In turn, a group of researchers from Geisinger Medical Center used over two million ECGs for training deep neural networks to pinpoint patients at a higher risk of dying within a year. The key finding is that algorithms were able to recognize risk patterns overlooked by cardiologists.

AI is expected to save human experts considerable time and cut the number of misdiagnoses. Paired with low-cost hardware, deep learning algorithms may potentially enable the use of ECG as a diagnostic tool in places where cardiologists are rare or absent.

Commercially available solution

Early stroke diagnosis from head CT scans

AI advancement and promised benefits

According to multiple studies, AI can be also successfully applied in diagnosing ischemic stroke caused by large vessel occlusion or LVO. And experiments with Google’s Teachable Machine showed that trained algorithms correctly identify the type of stroke in 77.4 percent of cases.

In most cases, AI algorithms sufficiently differentiate ischemic strokes caused by blood clots from hemorrhagic strokes caused by bleeding. Source: Young Scientist Journal

Potentially, AI trained by neuroradiologists may deliver a reliable “second opinion” to non-expert medical service providers so that they can make fast decisions and minimize damages.

Commercially available solutions

AI Stroke by Aidoc (based in Israel, FDA-cleared and CE-marked). AI Stroke package covers two types of stroke — ICH and LVO. The system automatically flags suspected cases, enabling radiologists to quickly decide on the course of action.

e-Stroke Suite by Brainomix (based in the UK, CE-marked). The AI-driven imaging software automatically assesses CT scans of stroke patients. Currently, the algorithm identifies only the ischemic stroke that amounts to 85 percent of all cases.

Barriers to ML adoption in healthcare

But besides financial issues, which are common for many fields, the healthcare sector adds industry-specific layers of complexities.

Regulatory issues

Shortage of data on new diseases

AI-assisted diagnosis for COVID-19 from computed tomography scans. Built in China, the intelligent system still lacks data to be broadly adopted. Source: medRxiv

Why is a wealth of data so important for the success of ML algorithms? Roughly, the more images of a pathology you run through the machine in the training stage, the better it can recognize particular anomalies on its own. For coronavirus, the current lack of historical data is worsened by another, more permanent problem — limitations on sharing health information.

Data silos and privacy rules

To address the problem of privacy, Google offered a new approach, called federated learning. It allows for training the current algorithm at different hospitals using local datasets. Then, the updates are sent to central storage to improve a shared model. This way, institutions exchange models, not sensitive data. However, the privacy-first technique is not without its pitfalls. For example, it requires hospitals to have infrastructures and personnel capable of training models.

Lack of standardization

Black box aspect and lack of trust

To illustrate the issue with trust, Erwin Bretscher puts an example of a project detecting cardiomyopathy, a disease of the heart muscle, from diagnostic images. “The anomaly is recognizable [to machines],” he explains, “ However, specialists often see a problem on scans, where everything seems to be fine. And most of the time they are right! Which brings me to the question: Can a computer replace human intuition? And who is responsible for the outcome?”

In the long run, the trust problem can be solved by so-called explainable AI (XAI) — an emerging area in machine learning that aims to provide domain experts with clear justifications for results produced by models.

The difference between today’s ML models and XAI. Source: DARPA

XAI solutions, currently developed, are simple and find limited usage. Yet, it is expected that such algorithms will eventually dominate in healthcare as they bring transparency into decision-making processes.

AI vs MD: who’s the boss here?

“AI can relieve the pressure on healthcare systems,” Erwin Bretscher adds. “ In many countries, the population is getting older and demanding more care, but the sector fails to grow equally.”

In the coming years, we’ll see more diagnostic solutions utilizing deep learning algorithms to bring enormous improvements to patient care. But who will make a final decision and bear responsibility? Apparently, a live professional: AI is still too young for this.

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