Discover how we’re helping healthcare companies drive their digital technology resolutions forward.
Part I: Healthcare and machine learning
This series aims to elaborate on the relationship of healthcare and medical field and new technologies. Throughout this journey, the reader will be accompanied by our guidance and view of the stories
From ancient time
To the layman of every country, medical industry is the pillar of the society. It is the protector and caretaker of the people, where one selflessly joins the effort of taking care of others, sharing the sentiment of human nurturing and mending. The Hippocratic oath, familiar to all herds, conceived by Greek physician Hippocrates(460-370 BC) and held sacred by doctors to this day, hints at the sacrifice, longevity, and the good-nature characteristic of the obelisk of human civilization since ancient time.
“Hippocratic Oath: : to treat the ill to the best of one’s ability, to preserve a patient’s privacy, to teach the secrets of medicine to the next generation, and so on.”
Strikingly, medical industry shares uncanny resemblances with the opposing force: war. Maybe since both are born out the need to fight, the people involved have to think of brilliant and innovative stance to face against odds. On this note, medical industry has undoubtedly been not only the pillar of societies, but also the technology tower-of-Babel. From health care research to healthcare technology, inventions and methodologies have cure, mend, prolong and improved human life from the atomic scale. Since wooden crutches, primitive surgical tools to stem cells, cancer treatment, artificial organs and so on, human have never stopped the effort to invent new ways to save more of its fellow in its long thousands of civilization years.
The new age
This mentality is carried on to the new age and became even more stellar. In the year 2021, advancement in healthcare and medical fields are uncountable and astounding in achievement, however, they share a similarity: the most cutting edges cannot be mentioned without the substantial involvement of AI, data, and machine learning
Computers which can learn without being programmed – that is the essence of machine learning. They are built upon mathematic models, which helps them to learn and adapt like human beings when exposed to new situations. Any situation, to be precise, and thus machine learning is ubiquitous.
As a non-exception, the medical and healthcare industries benefit from machine learning. They are seen in many places, but most prominently in the following:
- they push data integrity: decision making can be difficult in medical field when there is not enough historical information, and machine learning helps here by covering the gaps in healthcare information with its prediction, consolidated by experience learned from similar problems during training. Thus, historical data can now be complete and doctors can make decision about patient health and treatment with more certainty
- they produce predictive analysis: not only the past but the future, machine learning can elaborate on what to come with high level of confidence and accuracy. Prediction can range from estimating cases of flu in next month, predicting the number of drugs needed for a population in the next decade, or simply diseases a person might have during his life, from his personal biometric data.
- they helps create new medicine: in preliminary stage of drug discovery to the final production, machine learning contributes to success in finding a good path to producing a cure. Unsupervised learning can crawl through millions of paths to find the new drug for a disease, or optimizing the time to produce drugs by predicting the best manufacturing processes.
- they assist in record keepings: one example is nature language processing(NLP), which let doctors capture and record any medical notes, eliminating the manual paperwork. Automatic speech recognition(ASR) also aids in this effort tremendously, transcribing all spoken record between doctors or patients with high level of accuracy
- they identify diseases and produce diagnosis: This is a where machine learning shines: it can accumulate doctors experience and diagnose patient health in simple cases, or assist doctors in making conclusions in difficult situation. For instance, with the help of image processing and recognition, machine learning can identify potential diseases from MRI scan, CT scans and X-ray, then suggesting treatment options based on similar cases.