An astounding amount of time and tremendous resources are lost daily in the global healthcare systems.
Inaccurate diagnoses lead to tests that aren’t needed. Delayed diagnoses lead to delayed treatment. This leads to survival and remission rates that spiral downwards versus the outcome that would have been if the condition was identified properly earlier. There are false positives that come back on important tests. Clinical trials, experimental treatments and research is compartmentalized globally rather than one system leveraging on the insight of other experts across the world.
Machine Learning Opens Doors for Healthcare as a Whole
When healthcare meets technology. The innovation that stems from the collaboration brings forth astounding changes in the current reality when Artificial Intelligence and machine learning unite to revolutionize medicine as it was known. Computers run algorithms that scrub an insane amount of data, far more and far faster than any one human can accurately process, no matter if he/she is a doctor, a scientist, or even a genius. AI/ML can process all of this data and discover patterns and make predictions that influence diagnosis of disease and infection, plus instigate a treatment plan. The world of AI/ML meeting the world of modern medicine will create a planet with enhanced and advanced public health and safety.
The expected explosive impact that the improvements that the healthcare system will have for billions of patients and the potential financial benefit, healthcare is quickly becoming an investment focal point.
Major players in the industry such as IBM and Microsoft have begun their own AI healthcare projects. Additionally, there are hundreds upon thousands of start-ups and small organizations that have their own efforts to create AI/ML that will aid with healthcare.
The potential for monetary savings is tremendous. A report from McKinsey & Company reports that big data could save the healthcare industry and pharma industry upwards of 100 billion dollars annually. Improvements in the efficiency of clinical trials, better research avenues and tools, and clear and concise insight when making decisions. New tools will help insurance providers, regulators, doctors and surgeons, and consumers make wiser decisions in less time.
Machine learning algorithms serve to improve the data that they are given. And healthcare has an abundance of data to feed the machine. With various storage systems atop ownership and privacy red tape, there is no established system that makes it possible for healthcare as a whole to share data from clinic to clinic, clinic to hospital, hospital to skilled nursing facility, facility to pharmacy, pharmacy to pharmacy, etc. When this is accomplished, even just nationally, the results that healthcare professionals will glean will be insurmountable.
AI/ML, Disease Diagnosis, Disease Treatment, and Wound
AI healthcare applications are able to diagnose diseases such as jaundice in a newborn to heart disease in an older individual. The AI/ML tools that are hitting the market don’t just diagnose diseases, though, they also can recommend treatment. Bayer has been working alongside tech companies to develop software that will assist in the diagnosis of rare and complex medical conditions in addition to creating new medications to treat these very diseases. With hospitals and researchers as their partners, they are determining what the ML needs to analyze before a diagnosis can be made. The information that AI/ML utilizes include:
- symptom data
- causes of disease
- results of tests run
- radiology imaging
- physician reports
- and more.
Color duplex ultrasound scanning and other advances in imaging have paved the way for wound care experts to have more confidence in their confirmation of venous etiology and the following diagnosis of venous ulcers. One of AI’s strengths is the analysis of radiology images. AI can assist clinicians with diagnosing a wound quicker and improve on accuracy through pinpoint precision on the measurement of a wound, levels of exudate, protease, and making a determination on bacterial burden.
Clinicians are overloaded with new products and new research studies. Making the right choice based on evidence is a constant dilemma. AI/ML can analyze significant attributes from the file of a patient and then juxtapose those attributes to clinical guidelines, research from external sources, and historical data. AI will also compare products, assess the risk of wound healing treatment options, make an outcome prediction, and identify the treatment plan that will deliver the most optimal outcome for the patient.
The likelihood of AI replacing wound care and other clinicians isn’t really existent. A patient that has severe trauma after an injury or burn requires human compassion and support from a healthcare provider to battle the physical, mental, and emotional decline. Bedside manner that forms a strong bond between doctors and their patients is as important as the right medication and cannot be replaced by artificial intelligence.
AI/ML Changing the
Home Healthcare Industry
AI benefits patients who have chronic wounds. It gets most data from in-person consultations conducted in the home between office visits. It is highly beneficial to patients that live in rural regions because they can avoid making the long trip to and from their clinic. AI provides continual monitoring of the patient so that their clinician can gather follow up data efficiently and at a low cost. This gives the patient a sense of liberation as well.
Some researchers are turning to social media to connect people who have the same diseases to gather information on what treatments are being administered and how the patient is responding to them. Some use Facebook, some use Twitter. The data is gathered and AI is used to determine what treatments are most successful according to the patient’s report.
Health Epidemic Monitoring
When the Ebola outbreak came, an algorithm identified the outbreak nine days prior to the World Health Organization’s announcement. The computer read social media, news, and governmental agency websites globally and identified the outbreak before any human had. The more data the algorithm received , the stronger the evidence of an outbreak became.
AI/ML in healthcare continues to evolve and will have an increasingly substantial impact on disease prevention and diagnosis in the future. Customized drugs will be developed according to a patient’s individual unique DNA and new and improved treatment options for wound care and diseases will be developed.