Niche Applications of Artificial Intelligence in healthcare

“Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.“
-Max Tegmark, President of the Future of Life Institute

iReviews-AI-in-HealthcarePicture credits: iReviews

Artificial Intelligence has made its way to every field possible, steamrolling the processes along its way. One such field is healthcare. They say healthcare is a field that is not very rules based and a successful doctor is the one who leverages his/her experience to deal with complex and unseen cases. However, there are many low hanging fruits that are already being plucked by AI. This trend is being fueled by increasing digitization in healthcare data and advances in new algorithms. In this piece, we intend to give you a sneak peek into how AI is leading to improved healthcare for humanity. Below are some key examples of research areas and applications.


Aiding Histopathology


  • Virtual Slides Diagnosis

    Tissue-based diagnosis has seen technological advancement with the introduction of virtual slides. However, virtual slides demand a lot of time and efforts than that for viewing the original glass slides from the pathologists. This is the time taken in the selection of information containing fields of view. Artificial intelligence can automate the tissue diagnosis routine work. Deep Convolutional Neural Networks are already being used in this area. Automated diagnosis would save a lot of time wasted in supervising and the pathologists can focus on the serious cases.

  • Diabetic Retinopathy Treatment

    Diabetic Retinopathy (DR) is the fastest growing cause of blindness, with nearly 415 million diabetic patients at risk worldwide. If not caught early, it can lead to irreversible blindness. In “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs“, published by JAMA, Google presented a deep learning algorithm capable of interpreting signs of DR in retinal photographs, potentially helping doctors screen more patients in settings with limited resources.

  • Skin Cancer Treatment

    Sebastian Thrun’s lab at Stanford released an AI algorithm which detects Skin Cancer with very high accuracy. This algorithm was tested against 21 board-certified dermatologists. In its diagnoses of skin lesions, which represented the most common and deadliest skin cancers, the algorithm matched the performance of professional dermatologists.

Medical Diagnosis

AI algorithms can aid doctors in medical diagnosis.They can highlight key instances in a person’s previous health history. Incorporating AI into the medical field has the potential to change and vastly improve healthcare in its core. From improved diagnostic accuracy to better-optimized treatment plans, AI could be the key to better medical care for doctors and patients alike.
In August 2016, doctors at a hospital in Japan misidentified a 60-year-old woman’s leukemia. But IBM’s Watson examined a vast database of 20 million research papers and made a successful diagnosis in just 10 minutes. The AI-based system can be utilized to prune out the irrelevant data and help the doctor think more clearly focusing on the vital data.

Risk Prediction


The team of primary care researchers and computer scientists compared a set of standard guidelines from the American College of Cardiology (ACC) with four ‘machine-learning’ algorithms. These algorithms analyzed large amounts of data and self-learn patterns within the data to make predictions on future events which were a patient’s future risk of having heart disease or a stroke, in this case.
The results, published in the online journal PLOS ONE, showed that the self-teaching ‘artificially intelligent’ tools were remarkably more accurate in predicting cardiovascular disease than the established guidelines. This technology is a godsend for insurance companies by helping them do a more effective appraisal of health risks of a customer.


Picture credits: Nebraska Medicine
Picture credits: Nebraska Medicine

Applying AI for Radiology is harder as compared to Histopathology and hence we are yet to see groundbreaking results here. There is, however, a lot of work going on in situations where X-rays, CTs, and MRIs can be analyzed automatically, thereby giving radiologists a quick second opinion to consult with.
AI has already been used for Chest X-rays for direct diagnosis. Some of the other areas where AI aids diagnosis significantly is segmenting hip bones and lumbar vertebra for QCT/MRI in osteoporosis screening.
A Recent release of Stanford Medical-ImageNet is likely to start a revolution like what ImageNet did for normal images.

Automating Drug Discovery

Discovery of a new drug takes years of research, its launch takes even more time and money. Automating drug discovery through AI can tremendously reduce the cost and time as well.The average biomedical researcher deals with a huge amount of new information every day. It is estimated that the bioscience industry is getting 10,000 new publications uploaded on a daily basis from across the globe and among a huge variety of biomedical databases and journals. So, it becomes impossible for the researcher to process the entire information alone. Artificial Intelligence has a vital role to play in elevating the work of drug development researchers.

  • A study published in Cell Chemical Biology reveals a big data-based approach to detecting toxic side effects of a drug before it goes to the expensive clinical testing. In the approach called PrOCTOR, researchers analyze each drug using 48 different features to ascertain its safety for clinical use. The entire process is automated using machine learning.
  • A company named BenevolentBio has been doing research into Amyotrophic Lateral Sclerosis (ALS). The AI they’ve developed incorporated in the company’s Judgement Correlation System (JACS) reviews billions of sentences and paragraphs from scientific research papers and abstracts. JACS then links direct relationships between the data and regulates the data into ‘known facts’. These known facts are used to generate a large number of possible hypotheses using criteria set by the scientists. Based on these hypotheses, possible drugs are discovered. They have already managed to identify two potential drug targets for Alzheimer.

Some challenges before we conquer

However, it comes with its own set of challenges. Data is distributed across many isolated systems, sometimes not even in digital format. This unstructured, uncollated data is a roadblock to developing world class AI solution. Furthermore, healthcare is a regulated space globally. While regulations are necessary, it makes the access to data harder for innovation. The importance of handling patient data carefully can never be undermined. Seamless integration with the traditional systems also presents a challenge for startups and enterprises across the world.

The promising future!

Given the vast amount of data, healthcare looks to be the most promising sector that will be disrupted by AI. Given the massive impact that can be created by deploying AI solutions in Healthcare, it’s not surprising that some of the world’s leading tech companies are after it. The impending disruption will completely change the way patient care is delivered globally. We here are very excited about the potential of artificial intelligence in a space that is very close to all of our hearts.
Here’s to the better health of the planet!

ParallelDots is an Artificial Intelligence research and Deep Learning startup that provides AI solutions to clients in multiple domains. You can check out some of our text analysis APIs and reach out to us by filling this form here.

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