For instance, language processing. This helps physicians to grab and record clinical notes by eliminating natural processes.

Recent changes in machine learning increase healthcare access in all developing countries and introduce the diagnosis and treatment of cancer. According to an article published by AMA Journal of Ethics, AI and ML in healthcare now help in diagnosing skin cancer more accurately than a certified dermatologist. This study focuses on the additional benefits of machine learning, including diagnostics efficiency and speed.

Precision medicine, involving identifying methods for multifactorial disease, appears to be the frontier in this case. Most of the research consists of unsupervised learning and a large part of it is still confined to identifying the data patterns without predictions.

The best example of AI and ML technology being used today is the opioid epidemic. Artificial neural networks and support vector machines have been used for predicting malaria outbreaks. ​

For instance, low blood pressure and high fever can be caused due to a wide range of health problems. AI and MI excel in industries that are largely dependent on quantitative information. Making the correct diagnoses involves science, as well as art. This is the reason, even though algorithms are excellent when it comes to diagnosing malignant tumors, it can be difficult to diagnose a complicated condition from the common cold. To differentiate this, algorithms and experts both need experience when it comes to using Artificial Intelligence in healthcare.

• Data governance is a major issue that has to be addressed. Medical data is personal and isn’t easy to access. Therefore, it can be assumed that a majority of the people are wary of releasing details due to privacy concerns.
• A more transparent algorithm is required to cater to the strict regulations of drug development. People should also be able to get a glimpse through the black box to understand the reasoning behind machine outcomes.
• Encouraging a data-centric view and breaking down data silos across different sectors is of great importance. It will help in shifting the mindset of the industry towards embracing and looking for value in incremental changes over a longer period. Medicine and pharma companies have been reluctant to make changes that support the initiative unless there is a significant or immediate monetary value.
• Recruiting data science talent in the medicine and pharma industry and building strong skills is a prime necessity.
• Streamlining electronic records that at present are still fragmented and messy across databases will be an important step in ramping up customized treatment solutions.

• The clinical staff is busy. For instance, the intensive care unit has multiple patients in serious condition under their watch. So, restricted cognition and mobility during long-term treatment might affect the overall recovery of patients. It is crucial to monitor their activities. For improving results, Stanford University researchers and Intermountain LDS Hospitals have used depth sensors that are equipped with Machine Learning algorithms in the room of the patient for tracking their mobility. The technology has been accurate in identifying movements 87% of the time. Eventually, researchers are looking to offer alerts to the ICU staff whenever patients are in trouble.
• One of the primary challenges of drug development is to conduct successful clinical trials. Considering its progress, it can take more than a decade to bring a potentially life-saving and new drug to a market, as per a report in Trends in Pharmacological Sciences. Also, it might cost between $1.5 billion and $2 billion. About half of the time is spent on clinical trials, much of which fail. Using artificial technology, researchers can recognize the patients eligible for the experiments.

But using machine learning in healthcare comes with many challenges. Long-grained institutional practices and various cultures in organizations can’t be optimized simply by slapping an algorithm. Legacy Electronic Medical System and EHR running on-premises do not play well with other ones used by other organizations.

Organizations have to take into account the government regulations that keep changing. So making sense of large volumes being generated that are primarily unstructured is not an easy thing. This is the reason data scientists who have trained in the recent techniques and technologies are in great demand in the healthcare industry.

We have just scratched the surface on the possible impact of Artificial Intelligence and Machine Learning in healthcare. However, one thing is pretty clear that AI and ML use cases in healthcare industry are important for its future.

With an increase in the demand for the best healthcare facilities, organizations need to hire professionals with deep knowledge of growing technologies like AI and ML. ResolveData is one of them.