10 Use Cases of Machine Learning for Medical Diagnosis

Machine learning for medical diagnosis helps detect life-threatening conditions in the earliest stages. Learn more about 10 ways to use machine learning in diagnostics.

Diagnosing diseases precisely is the cornerstone of healthcare. When physicians make bad judgment calls, their negligence or lack of concentration can result in life-altering complications and prolong patient recovery.

Statistically, 10% of patient deaths and 17% of complications are a direct consequence of misdiagnosis. How can healthcare service providers improve the precision of each diagnosis? Making use of artificial intelligence and machine learning is a promising way to mitigate the issue.

In this post, we will take a closer look at the ways to use machine learning for the purpose of medical diagnosis and get to know the companies who are leading the adoption of intelligent technologies.

Reality of Applying AI For Medical Diagnosis

Throughout the last decade, artificial intelligence has penetrated healthcare at a growing pace. The projections for this technology’s growth in the next five years are promising, as well — in fact, researchers project a 44.9% growth rate for medical AI.

How do healthcare professionals make use of artificial intelligence, machine learning, and the other powerful tools at their disposal? Here’s our review of the top 10 applications related to machine learning for medical diagnosis.

In oncology, the importance of detecting a malignant tumor on time is vital. This is why the accuracy and precision of the diagnosis are crucial in this field.
Machine learning helps oncologists detect the disease at its earliest stages. With the help of tools like DeepGene, medical professionals can detect somatic mutations easily (a somatic mutation is an acquired change in a genetic code of one or more cells). Artificial intelligence pinpoints mutation markers faster and with higher accuracy than humans do.

In addition to pinpointing the tumor, machine learning can accurately determine if it’s malignant or benign in milliseconds. Although computer-based predictions aren’t error-free, the accuracy of classification is impressive at 88%.

Given the worldwide shortage of pathologists, there’s a considerable need for adopting machine learning to make progress in this field. The need to process large datasets also makes Pathology extremely lucrative for artificial intelligence implementation.

Here are the most promising ways of using machine learning to indicate medical diagnosis:

  • Improving the precision of blood and culture analysis using automated tissue and cell quantification.
  • Mapping disease cells and flagging areas of interest on a medical slide.
  • Creating tumor staging paradigms.
  • Improving healthcare professionals’ productivity by increasing the speed of profile scanning.

In dermatology, artificial intelligence is used to improve clinical decision-making and ensure the accuracy of skin disease diagnoses. Physicians hope that machine learning implementation in this field will reduce the number of unnecessary biopsies dermatologists have to put patients through.

There are plenty of functional machine learning implementations in Dermatology, namely:

  • An algorithm that separates melanomas from benign skin lesions with higher precision than that of a human.
  • Tools that track the development and changes in skin moles, helping detect pathological conditions at the earliest stages.
  • Algorithms that pinpoint biological markers for acne, nail fungus, and seborrheic dermatitis.

Recently, artificial intelligence has helped geneticists progress significantly in the transcription of human genes. Although the Human Genom Project is the poster case of healthcare and technology joining forces for potentially revolutionary research, it’s not the only way to use machine learning in medical diagnosis.

Machine learning and AI technologies are key players in preventive genetics. Scientists increasingly rely on algorithms to determine how drugs, chemicals, and environmental factors influence the human genome.

Last but not least, geneticists are hopeful that they will be able to improve the efficiency of gene editing, changing DNA fragments to protect a fetus from the impact of a mutation or reverse its effect.

Since the scientific community has repeatedly expressed ethical concerns regarding gene editing, its use in genetics is limited to fighting diseases that are considered incurable. At the moment, gene editing researchers are focused on fighting Cystic Fibrosis and Huffington Disease.

Statistically, mental health disorders are one of the costliest conditions to manage in the United States. Research shows that 1 in 5 adult Americans is affected by a mental disorder. The impact of leaving these conditions untreated or misdiagnosed is disastrous: low productivity, increased health spending, and lower overall life quality.

Artificial intelligence can have a groundbreaking impact on mental health research and the efficiency of medical diagnosis through machine learning. The top applications of innovative technologies in the field are:

  • Personalized cognitive behavior therapy (CBT) fueled by chatbots and virtual therapists.
  • Mental disease prevention by creating machine learning tools that help high-risk groups avoid social isolation.
  • Identifying groups with a high risk of suicide and providing them with support and assistance.
  • Early detection of mental disorders using machine learning and data science: diagnosing clinical depression, bipolar disorder, anxiety, and more.
  • Impact on research

Neuroscience and neurology benefit from implementing artificial intelligence in gathering, processing, and interpreting research data. From analyzing scans to providing insights about the human brain and detecting behavior patterns, innovative technologies are vital to pushing the frontier of neurological research.

Stroke prediction and recovery monitoring

Early stroke detection is another powerful application of machine learning for medical diagnosis. Technological algorithms help differentiate between resting and stroke-related paralysis, predict the recovery curve of ischemic stroke patients for over 90 days, and monitor early-onset stroke patients for 48 hours after hospitalization.

Research on degenerative conditions

The scientific community should not underestimate the impact of machine learning for research on Parkinson’s, Alzheimer’s, and other degenerative diseases. Some algorithms help determine the severity of a disease in an individual patient by assigning them corresponding scores. Understanding the current stage of the disease and the pace at which the condition is progressing helps physicians improve the quality of clinical care and the patient’s life.

Artificial intelligence has the potential to reduce the length of an average ICU stay by predicting early-onset sepsis and adjusting ventilator and other equipment settings according to a patient’s conditions.

Using artificial intelligence helps doctors avoid poor judgment calls — premature extubation or prolonged intubation — that have a strong link to raising ICU mortality rates.

In addition, machine learning in ICU can help physicians identify high-risk patients to make sure no early deterioration sign is left unnoticed. Innovative technologies can provide physicians with insights into patients’ well-being inside the ICU. For example, through the use of technology, intensive care physicians discovered that delirious patients are more sensitive to light than to noise.

The diagnosis of Ophthalmology conditions has a lot of room for machine learning optimization. Some of the latest innovations that these healthcare centers have adopted are:

  • AI-driven vision screening programs that help provide a point-of-care medical diagnosis based on machine learning for Ophthalmological conditions.
  • Identifying Diabetic Retinopathy and providing physicians with treatment insights by analyzing patient data (in 2018, the FDA approved the first among these machine learning scanners for clinical use).
  • Early-stage diagnosis of Macular Degeneration with the help of deep learning algorithms.
  • High-precision glaucoma and cataract screening.

Since Type I and Type II diabetes are highly widespread conditions, the amount of data researchers have at their disposal is tremendous.

To advance research in this field, scientists need to focus on consolidating these insights and putting them into a single framework. This is one of the primary machine learning goals in the field.

Over the last decade, the range of machine learning application examples for diagnosing and treating diabetes has grown exponentially:

  • Using vector machine modeling and building neural networks for pre-diabetes screening.
  • Creating tools for managing personalized insulin delivery, as well as artificial pancreas systems.
  • Predicting treatable complications in diabetes patients to improve the quality of their lives.
  • Identifying genetic and other biomarkers for diabetes.

Artificial intelligence allows healthcare professionals to increase the scale of medical diagnoses using machine learning and shift from analyzing individual cases to monitoring communities and predicting disease outbreaks.

Artificial intelligence and data science help Epidemiologists, public officials, and healthcare facility managers aggregate social media, clinical, and other patient data to determine healthcare trends and address the pain points of community residents.

In some countries, AI-based prediction monitoring tools are adopted at a national level (e.g. Public Health Agency in Canada that helped detect SARS and MERS outbreaks in the earliest stages).

After the outbreak of COVID-19, judges in Kentucky and West Virginia allowed for the marking of COVID-positive patients with ankle bands to track their movements.

A robust tech infrastructure is necessary to implement artificial intelligence in medical diagnosis. The good news is, there is no shortage of companies focused on introducing innovative technologies to healthcare. Let’s take a closer look at the frontrunners in the field:

A few years ago, Google Health decided to implement artificial intelligence to build a platform for cancer diagnosis.

The company merged with the team from DeepMind to build an algorithm for diagnosing breast cancer. The resulting technology was a huge success, beating the diagnosing precision of human radiologists.

Other exciting projects at Google Health include blood sugar detection tools and genetic data gathering.

This emergent Silicon Valley-based startup is transforming healthcare using data science and predictive analytics. Rather than focusing on direct doctor-patient interactions, the company offers tools for smart operational decision-making.

CrowdMedX Health helps healthcare facility managers plan budgets, gather and process electronic medical records, as well as provide a centralized hub for storing medical insights.

IBM ventures both in diagnostics and pharmacy, introducing artificial intelligence to both fields. Watson Health recently presented an innovative service for early breast cancer detection. The company also developed the platform Biorasi, which optimizes drug production.

It’s worth noting that IBM got its share of backlash for oncology-related projects. The Wall Street Journal wrote: “more than a dozen IBM clients and partners have shrunk or halted the company’s oncology projects.”

Corti is an AI-based software that helps ER physicians gather insights from patient conversations. On top of analyzing the contents of the call, the system captures the intonations of a caller’s voice and tracks background noise to provide medical professionals with a full understanding of the situation on-site.

According to these statistics, medical professionals in Denmark, where the platform is developed and deployed, can identify a hard attack over the phone with 73% precision. The accuracy was considerably higher than that of a human-only team.

Our Experience

Biosense Webster

Biosense Webster is a neural-network-based tool that helps physicians detect cardiac excitation. After training algorithms to analyze over 3,500,000 ECG records, the team has achieved 96% cardiac activation detection. The platform is now in use by Biosense Webster, an Israel-based member of the Johnson & Johnson group.

The platform for the Society of Imaging Informatics in Medicine (SIIM)

The Fayrix team was approached by a leading medical imaging research institution in Spain. Based on the organization’s request, we created a solution that uses scans and clinical data to determine the causes of lung collapse in patients and identifies the boundaries of pneumothorax.
Fayrix developers used Keras and Tensorflow neural network libraries as the groundwork for this platform.

Final Thoughts

Medical diagnostics is a booming industry with numerous opportunities for machine learning integration. Innovative technologies are widely deployed in different healthcare sectors, helping detect cancers, neurological conditions, pathogens, and other significant changes in the human body.

Leveraging the power of machine learning is a way for healthcare facilities and business managers to improve the standards of care, optimize internal processes, and maximize staff productivity. This is why building a machine learning project is an investment that will bring healthcare professionals a high return.

To build innovative digital health solutions from scratch, scientists and managers should join forces with tech partners. Fayrix is an experienced team of developers who have completed dozens of machine learning projects in healthcare and other fields.

We are looking forward to working on your project — get in touch with us to discuss the details!

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