Machine Learning is Making Strides in Medicine
How a form of AI is transforming healthcare
Early in the Covid-19 pandemic, scientists scrambled to work out the genetic code in certain molecules of lungs and intestines, to see which cells were most vulnerable to infection. A new technique published this year called BITFAM uses machine learning (ML), integrating different types of biological data to recognize discrete biological functions and regulatory mechanisms of cells.
In other words, ML, a common form of artificial intelligence (AI) quickly analyzes and finds patterns in massive amounts of data, providing scientific insights to get to solutions faster, like creating a new drug to cure a virus.
There are many more examples where ML is a champion in medicine and healthcare, improving operations and saving time and money. ML allows medical professionals to create more efficient and holistic care plans, and is transforming healthcare.
A quick intro to machine learning
Algorithms are at the core of machine learning applications. A collection of instructions tells the machine to perform a specific set of tasks. Algorithms can analyze and “learn” from the data without human intervention. ML algorithms improve over time, independently increasing the accuracy. ML does this in three steps:
- Representation: Data needs to be brought into a form that a computer can handle.
- Evaluation: Data is reviewed to determine if classifications are useful.
- Optimization: As part of the process, the algorithm finds the best model for the most effective and accurate outputs.
Machine learning applications in healthcare
Machine learning provides quick, powerful and accurate functions to provide better health outcomes, and is being used in a variety of ways. One example applies a complex version of ML, called deep learning, to replicate how the brain functions. This type of ML is advancing diagnostic capabilities used in radiology and imaging, and can detect, recognize and analyze cancerous lesions. Applications are also being developed to provide diagnostic tools for diabetic retinopathy and analytics to predict breast cancer recurrence.
On the administrative side, ML streamlines record keeping, and manages robust health care records. Doctors don’t have to transcribe their notes- natural language processing, enables capturing, recording and filing spoken clinical notes. Systems are speedier and easy to use, aiding in medical decision support, automating image review and integration with telehealth technologies.
Other applications of machine learning in medicine include:
Disease Identification and Diagnosis
Machine learning algorithms are adept at detecting patterns associated with diseases and health conditions by analyzing thousands of healthcare records and patient data. This is a significant change with things like early cancer diagnosis. Scientists at MIT have shown where they can predict breast cancer development years in advance. This year, MIT published information on a new ML application, DELPHI, that continually scans global data, collects and categorizes all published research on a topic within a short time frame of it being released. This means doctors can access the latest breakthroughs in medicine and new diseases at the touch of a button.
ML helps Medical Imaging Diagnosis
High-end imaging technologies, such as CAT scans and MRIs, create incredibly high-resolution outputs. Sorting through the megapixels and data can be a challenge for radiologists and pathologists.
Machine learning has shown its value in helping clinical professionals improve their productivity and precision. Common use cases for machine learning in medical imaging include identifying cardiovascular abnormalities, detecting musculoskeletal injuries and screening for cancers.
If you haven’t yet seen robotic aided surgery, it is amazing to watch. ML driven robotic equipment and tools can reduce human error, especially in more complex surgeries like repairing spinal cord injuries. Other surgeries are less invasive to complete. Surgical robots can provide help before surgery too, planning workflows for procedures.
Robotic Patient Support Tasks
Robots can help patients regain physical abilities. Examples include an exoskeleton robot that can help a paralyzed patient walk again, or one that fills the role of a smart prosthesis. Bionic limbs not only restore physical functionality, they are often more accurate and reactive than the original body part.
Could a machine deliver more personalized health care than a human physician? While we will probably always need and want a human’s intuitive care, ML can not only analyze health data from a vast amount of resources better, it also can review genetic data to create a more personalized care plan. With the shortage of health care practitioners today, this service reduces the human burden in analysis of vast amounts of information. Working in partnership with ML, doctors can efficiently create advanced personalized care plans.
The future of healthcare technology
Here are a few headlines regarding how artificial intelligence is changing the future of healthcare.
New Jobs. The newest field of medicine is called Health Informatics Professionals. The role involves applying in-depth knowledge of technology to manage and utilize data to improve patient care.
Virtual Reality. Virtual reality (VR) is a game changer in training new doctors in medical procedures. Switching the headset to the patient side, VR is helping speed recovery in physical therapy by providing customized activities that are more enjoyable than a standard workout.
Augmented Reality. Forget studying corpses. Augmented reality (AR) gives students an opportunity to learn from real patients while doctors are performing surgery. Also, the entire anatomy of a human body can be studied in a realistic four-dimensional space.
Wearable Tech. The stylish bands that started counting our steps less than a decade ago are evolving to become a high-tech physician’s assistant. Wearable technology can provide doctors everything from your heart rhythm and rate to blood pressure and temperature, but that is expanding to so much more. The technology to sense and analyze data in wearables is getting a lot of funding right now. When machine learning is added in to crunch your past health information, genetics and data of disease and conditions from around the globe, you may have a powerful diagnostic tool strapped to your wrist in the not too distant future. Oh, and it can still help you be more fit.
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Genome Sequencing. According to the National Human Genome Research Institute, sequencing DNA means determining the order of the four chemical building blocks – called “bases” – that make up the DNA molecule. The sequence tells scientists the kind of genetic information that is carried in a particular DNA segment. A genome is the total of DNA information in an organism.
Machine learning helps medical professionals analyze genomic data faster, potentially finding genetic mutations. This information is used to diagnose conditions that can lead to disease, such as cancer. Genome sequencing can serve as an early warning sign, allowing doctors to mitigate the impact of illnesses.
The first-ever human genome sequencing project cost more than $3 billion, now the testing can be done for less than $1000. It is still not a common test in medical practices, except in some areas like cancer, but as costs decline and technology advances, expect to see genome sequencing more commonly applied to treat the world’s most serious diseases.
Nanotechnology. The National Nanotechnology Initiative describes nanotechnology as “the understanding and control of matter at the nanoscale, at dimensions between approximately 1 and 100 nanometers.” In other words, very, very tiny.
So how does nanotechnology apply to healthcare? One example is in drug delivery. Nanotechnology can help deliver medicine to targeted regions, bypassing areas in the human system that aren’t affected by diseases.
3D Printing. The technology of 3D printing, backed by the data power of ML can be applied in a variety of ways, from drug manufacturing to creating complex human tissues and organs. Medical components can be personalized to fit the medical need, improving healthcare quality at a lower cost.
Transforming the Healthcare Industry
According to a report published by Allied Market Research, global AI in the healthcare market generated $8.23 billion in 2020, and is estimated to reach $194.4 billion by 2030, growing at a rapid compound annual growth rate of 38.1% from 2021 to 2030.
However, if alarm bells are not already going off regarding safe keeping of your personal medical data, they should. As that data collection increases, a full and complete profile of patients, including potentially their DNA data, is stored and must be cared for. Like in other fields where data is collected, personal healthcare data is extremely valuable to external companies. This is a huge topic, with security and ethical concerns for another article.