The Intelligence is Artificial, but the Healthcare Achievements are Super-Natural

by Bizemag Editors

The Intelligence is Artificial, but the Healthcare Achievements are Super-Natural

AI is the big new technology and while it is in a relatively early stage of development, it has already started to show excellent results. The number of industries that stand to benefit from the use of AI are many in number. And one key field where AI is already revolutionizing the entire industry is healthcare.

AI together with its associated technologies of ML (Machine Learning), DL (Deep Learning and NLP (Natural Language Processing) is making is easier for medical professionals in Identifying healthcare needs and solutions. Working at tandem, they all together make such healthcare functions more accurate by finding patterns in healthcare data easing the work of industry professionals and helping them make a more informed choice.

The Role of AI in Healthcare

Healthcare organizations have huge amounts of data stored in their system which encompass the various aspects of healthcare like diagnostic and other types of images, detailed information on clinical research trials and even medical claims. AI can use such data to analyze them and come up with patterns and insights which the human mind would have missed in almost all cases.

AI algorithms can learn how to label and identify patterns in data. NLP chips in too here by allowing such algorithms to isolate the data concerning the task. With DL technology the system can further analyze the data and interpret them. AI in healthcare is big business and like its impact on healthcare and human well being the market is booming. According to healthcare industry experts Frost & Sullivan, AI healthcare systems market touched the $6.7 billion in the recent past. Compare this to its $811 million valuation in 2015, the growth is almost exponential!

AI is already playing a critical role in the functions of many healthcare stakeholders like the following:

Researchers- Researcher, clinician and data manager teams that are part of a clinical trial can now hasten the whole process of medical coding search and confirmation. This process is particularly important in conducting clinical studies and arriving at a particular conclusion.

Patients and general people- People paying for healthcare services as health plans can also get customized plans through AI. People looking for customized healthcare solutions can simply connect to a virtual agent through a conversational AI.

Clinicians- Healthcare professionals can enhance patient care besides customizing them through going through medical data that can predict and diagnose diseases faster.

Top Uses of AI in Healthcare

  • You can perform analysis of medical images with the technology

Medical professionals already use AI healthcare tools to perform preliminary analysis of the health condition of a patient. Clinicians can then review medical images and scan results which in turn lets cardiologists and radiologists gain essential insight that allows for proper prioritization according to the severity of the patient condition. AI lets them avoid errors which might creep in as they go through electronic health records or EHRs. Finally, AI helps clinicians make more precise diagnosis about the patient condition. prioritizing critical cases, to avoid potential errors in reading electronic health records (EHRs) and to establish more precise diagnoses.

Additionally, when researchers conduct clinical studies, enormous of medical data and images are created that need to be reviewed. AI can help medical professionals in this, by analyzing the datasets much faster and let them stand in comparison to other studies. While doing so AI algorithms are able to identify patterns and find out deep connections which usually the human mind might not have noticed. It helps medical personnel related to medical imaging track critical information much faster.

  • Development of medicines becomes less costly

Historically there have been records of supercomputers analyzing huge databases of molecular structures and predicting which combination of molecular structures might and might not be effective as a cure for various diseases. Such ai algorithms make use of CNNs or convolutional neural networks the same underlying processing structure that enables self-driving cars like those of Tesla.

Using this technology, the Atomnet system could predict accurately protein and small molecule bindings through the hints provided by records of millions of experimental measurements and several thousands of protein structures. The CNN-based system succeeded in its objective of creating an ai process that can identify medicine candidates which are both safe and effective from an existing database, and significantly cut down on medicine development costs.

AI can analyze even unstructured data

The huge amount of data associated with healthcare and medical records makes the task of physicians in providing quality patient care in tune with the latest medical advances a very, very challenging task. ML models can scan curated EHR and biomedical data swiftly thereby giving physicians the information they need.

Often healthcare data and patient medical records come in the form of complex unstructured data. It can be a daunting task to access and interpret unstructured data due to its very nature. AI comes to the rescue of healthcare professionals in such scenarios as it can seek, collect, and standardize the stored data no matter how it is formatted. This makes it easy to perform repetitive tasks. Clinicians are thereby able to provide accurate and customized medical treatment plans for patients without having to shift through huge amounts of unstructured, paper formatted EHR data.

You can build complicated and consolidated platforms for drug discovery with AI

AI algorithms cannot only identify new drug applications but also trace their toxicity and track how they work. Such applications of AI technology helped in the creation of drug discovery platforms. Such platforms not only discover new drugs but also lets pharmaceutical companies repurpose their existing catalog of medical drugs and bioactive compounds.

The platform in question combines the latest developments in fields like data science, biology, and chemistry. It assumes great potency in drug discovery through the automation enabled by AI technology that lets it create 80 TB of data weekly collected from more than 1.5 million experiments that the ai tool conducts each and every week.

This specific ML tool is both free from human bias besides having the ability to get insights from datasets that are otherwise too complex for human beings. Companies are especially keen on repurposing existing drugs as it saves the cost of creating new drugs from bottom up.

AI can predict the onset of kidney related diseases

One kidney condition which is particularly difficult to detect by clinicians is Acute Kidney Injury. The fact that it can deteriorate very fast and assume life-threatening proportion makes it a particularly dangerous proposition. In fact, a significant 11% of patients die from such failure. Swift treatment or even better early prediction helps in life-long treatment of such conditions and reduces dialysis costs.

To address the issue, the US Department of Veteran Affairs teamed up with DeepMind Health in 2019 to create an ML tool that can predict the onset of the condition 48 hours before it can be identified by traditional healthcare systems. The tool performed exceptionally well in its objective by 90% success rate of detection of AKI 48 hours before traditional care systems.

AI can perform medical predictive analytics from collected data

Clinicians can now create more effective workflows, treatment plans and make better medical decisions thanks to the predictive functions of AI when used with EHR data. Further ML and NLP technologies can collect the complete medical history of a patient in real time. Such data can then be used to link them with symptoms of diseases, chronic conditions and even family diseases. AI can then use its predictive analytics functions to predict the onset of diseases and treat them before they become threatening or obtrusive. Essentially such functions let physicians predict chronic diseases besides tracking heir progression rates.

CloudMedX is a leading medical tool that deals primarily with unstructured data which the tool stores as notes. Such data ranges from being clinical notes, hospitalization notes, discharge summaries to diagnosis notes amongst others. HER data is then combines with such notes to create clinical insights to help healthcare professionals do their work. The AI-based healthcare solutions provided by the company have already been successfully used in treatment of several high-risk diseases like:

  • Pneumonia
  • Renal failure
  • Hypertension
  • Congestive heart failure
  • Diabetes
  • Liver cancer
  • Stroke
  • And even orthopedic surgery

It all means that even if the intelligence is artificial, it surely confers super-natural benefits for us human (organic) beings!

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