Healthy Futurism

  Photo by National Cancer Institute on Unsplash Machine Learning is making sweeping improvements to many industries, healthcare being one of them.  The impact ML can have on a sector or industry is as unique as the industry itself. Pharmacology is able to refine products by miniscule amounts to ensure quality and safety. The layer of coating that covers pill tablets is being reevaluated for accuracy by ML's capacity to find novel patterns in vast amounts of data. Resource management in hospitals is able to track inventory in real-time, giving physicians updates on what supplies are available. This is also helping in streamline the patient intake process. Radiologists are using AI to help identify chronic pulmonary hypertension faster using ML to find new pattern indicators. Personalized medicine, or precision medicine, is using ML to create treatment plans specific to a patient with increasing accuracy using predictive analysis.  So where does this leave business managers? The tim

  We live in a technological and data driven world in this new age where AI is poised to have a prominent effect in the future of medicine and healthcare. Several factors have come together in the past few years to support the quickening of AI developments of medicine which include the amount of healthcare data collected in recent years, low cost of high level computing which can process large datasets, the increasing prevalence of EMR and overall advances in computing technologies which have all fueled AI in drug development . With such advancements, the cost of implementing AI have reduced significantly because of which it would be easy for more healthcare facilities to use AI so we can say that AI in healthcare and drug development is here to stay .

  Machine Learning and especially Artificial Intelligence can give the medical and healthcare sector a massive boost. Digitalization could transform healthcare systems to become more sustainable and cheaper, faster, and more effective. It could even win us the battle against diseases like AIDS or Ebola or lead to healthier individuals and communities. It is important to note that healthcare needs to shift to be more technology-intensive amid this pandemic now more than ever, and the healthcare industry's current state will allow AI to thrive once integrated.   Besides Artificial Intelligence, the most prevalent technologies used in the healthcare sector include virtual reality, augmented reality, healthcare trackers, genome sequence, medical tricorder, nanotechnology, robotics, and 3-D printing. All these accounts for machine learning in the healthcare sector would almost certainly lead to cost reduction and increased time efficiency.   For instance, the conventional drug developme

Here are few of the barriers to adoptions of Artificial Intelligence 1. Government Re gulations The Current FDA regulations designed to protect humans from harm by machines rate AI as a class 3 risk to humans. This will make it difficult to change laws to allow AI increased direct Interaction with patients. 2. Accountability  There are laws in place to protect humans working in the medical field in the case they make a mistake, but   what happens when AI makes a mistake?   These mistakes could be due to a missed update or incorrect data.  In these cases, it is currently unclear who would be at fault: The developer of the machine or the hospital using the machine?  3. Data Integrity   Data integrity is the accuracy and completeness  of the collected data, and data is what drives AI’s capacity for Machine Learning. The data that influences ML must have a wide variety of different classes, sources, and types. To be useful it must also be representative of relevant populations, as there ar

Health care is the center of our world right now. But it has been the center of many people's world for decades. If you work in the Pharmaceutical industry you know how much money, resources, and time is constantly being utilized by the field. Resources are still stretched thin across the Research and Development sector, the sector responsible for finding new drugs to fight diseases.  The drug R&D pipeline is costing companies and investors more and more financially, but it is paying off.  There has also been a steady increase in drugs discovered and being investigated. Lets take a look at the Global Trends in Big Pharma...   Curious to know more?  We got the data needed to make these charts from Evaluate and PharmaExec !  Follow the links below.. EvaluatePharma- World Preview 2020, Outlook to 2026 Pharm Exec's Top 50 Companies 2020 Published by Michael Christel July 8, 2020

   These are some of the few areas where machine learning can be induced to present better information regarding health sectors.   1.  Information Management There is a constant rate of change in how the technology changes. As it has been said in Moore's Law that every second year, the number of processors in an integrated circuit will double, resulting in more Computer processing power.  Such rapid fast processing has led to the development of machine learning within Computers leading to the development of Artificial Intelligence.  Image Via Google As time proceeds. Many of the health care sectors will have improved Database systems and well as have Efficient DBMS which would lead to an increased and effective information Management within the hospitals, Clinics and Pharmaceutical companies. 2. Clinical Decision Support System  As of right now, Majority of the decisions are carried out by doctors who have to go through a hurdle of obtaining the right information for a certain pati

Lets look at the Pharmaceutical Industry for a minute...  Its a complex and competitive world out there for a multitude of reasons: There are time limits on patented treatments, customers have more bargaining power, and we are moving on to tackle more complex diseases. The more complex the disease, the more complex the research and development (R&D) phase for treatments. The R&D pipeline for treatments of rare, chronic, degenerative diseases is lengthy and costly, with many false starts and do-overs involved. How does it work? A simplified version is that researchers test the interaction of different molecules to gauge is a combination would be effective in killing the molecules of the disease in question. The process is traditionally done by human researchers using vast amounts of data about the properties of molecules to see the interaction between multiple molecules, and then their interaction with the disease.  As you can guess, its an extremely time consuming and costly pr

There are Wide variety of Companies that have implemented Machine learning to improve health sector and increase efficiency to Fasten our end-time processing and improve Online Analytics Processing (OLAP).  Let us begin by analyzing Big three companies, namely Google, IBM and a new startup from CloudMedX health.  1. Google - DeepMind. Google has been working on its application Google health which provides users a daily detailed description of how they have been working out, what their achieved goal is. Now Google has created a new subdivision within Google health to Implement DeepMind. DeepMind Logo Via Google DeepMind's System Integration allows DeepMind to access Google's Vast Database to ease the method of Data Mining patients health record.  The purpose of Deepminds Implementation is to eliminate the cost of extraction for Patients health record and be as efficient in helping Health professionals diagnose the individual condition faster and effectively.  2. IBM's Watson