Automation in Medical Science

Automation in Medical Science
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Applied Machine Learning in Healthcare

Google’s machine learning algorithm to detect breast cancer :

Machine learning in medicine has recently made headlines. Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. Google is using the power of computer-based reasoning to detect breast cancer, training the tool to look for cell patterns in slides of tissue, much the same way that the brain of a doctor might work. New findings show that this approach — enlisting machine learning, predictive analytics and pattern recognition — has achieved 89 percent accuracy, beyond the 73 percent score of a human pathologist.

Stanford’s deep learning algorithm to detect skin cancer :

Stanford is using a deep learning algorithm to identify skin cancer. They made a database of nearly 130,000 skin disease images and trained their algorithm to visually diagnose potential cancer. From the very first test, it performed with inspiring accuracy. Although this algorithm currently exists on a computer, the team would like to make it smartphone compatible in the near future, bringing reliable skin cancer diagnoses to our fingertips.

Robot Assisted Surgery

Robot-assisted surgery became a viable option in 2000, when the Da Vinci Surgical System — a minimally invasive robotic surgeon that is capable of performing complex surgeries — was approved by the FDA. Since then, over 1.75 million robotic surgery procedures have been performed, with “better visualization, increased precision, and enhanced dexterity compared to laparoscopy” according to the NIH. The Da Vinci system average cost is between $1.5M and $2M, which makes it quite unaffordable for small and medium sized hospitals.

It’s not replacement; It’s Displacement

While it’s understandable that doctors are concerned about medical automation — the reality is that machines will not replace doctors; they will just displace them. Patients will always need the human touch, and the caring and compassionate relationship with the people who deliver care.

There’s One Thing : No Machine Can Do Better Than a Doctor

Machines can only learn from precedent; they cannot ideate new ways of diagnosing, they cannot identify new diseases, and they cannot hypothesize new treatment methods. Because of this, the role of the doctor in our society will always be privileged, and will never disappear.

One serious problem is that of expectation of what AI can really do. At the end of the day, an AI system is educated and trained to solve a particular problem and that is pretty much its entire universe.

 These systems are not humans, who can freely interact with their environment. 

They are machines, not people. The question is no longer whether AI will fundamentally change the workplace. It’s happening. 

The true question is how companies can successfully use AI in ways that enables, not replaces, the human workforce, helping to make humans faster, more efficient and more productive.

Data drives all the algorithms on which the automated machines work

As more data is available, we have better information to provide patients. Predictive algorithms and machine learning can give us a better predictive model of mortality that doctors can use to educate patients. But machine learning needs a certain amount of data to generate an effective algorithm. Much of machine learning will initially come from organizations with big datasets. Health Catalyst is developing Collective Analytics for Excellence (CAFÉ™), an application built on a national de-identified repository of healthcare data from enterprise data warehouses (EDWs) and third-party data sources.

Human touch

Many patients feel that being touched is important to getting better

Compassion can reduce pain after surgery, improve survival rates and boost the immune system. …

Patients have significantly better outcomes when their physicians score high on empathy.


An endoscopy is a procedure where a small camera or tool on a long wire is shoved into the body through a “natural opening” to a search for damage, foreign objects, or traces of disease.

Even more impressive are so called “capsule endoscopies” where the procedure is boiled down to the simple act of swallowing a pill-sized robot that travels along your digestive tract gathering data and taking pictures that can be sent directly to a processor for diagnostics.

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