I really loved my interview with Pooja Kumar. Pooja is an investor for clients in Healthcare, and she knows a lot in the industry of AI in Healthcare. She was very helpful with centering where I should drive my research, and giving more detailed examples of how AI is improving Healthcare, and how that can improve, and how we can centralize AI in Healthcare with humanity. Here are some examples of what Pooja thought about how AI is revolutionizing Healthcare, and where and how we should adapt to improve the technology for the future:

• AI is starting to revolutionize healthcare, it hasn’t revolutionized Healthcare yet. AI will most likely develop statistically over the next couple decades. The context is that she works with doctors and investments for Healthcare, some are AI, so in the field of statistics is where she is an expert on.
  • Examples of ways that AI will advance and revolutionize healthcare in the future:
    • Hospital Systems, doctor advancements and analysis, and Institutions of AI research is where AI in Healthcare statistically is predicted to evolve.
    • Can technology replace the less clinical tasks that people can do
    • Technicians are wondering if AI can read radiology waves, and transfer them and analyze them to create an algorithm or a suggestion using core ML and Deep Learning platforms.
• Generally in 10 or 20 years, AI will be at a level that is incomparable to humans
• AI hasn’t had a major impact on patients, it is more at the level of watching over things that are in place
• Natural Language processing is good to pull information like clinical notes for AI Finance in Healthcare. Generally the Finance, numbers, and Natural Language processing are the main components and more advanced aspects of AI as of now, in Healthcare.
• Diagnostics of AI:
  • AI in Diagnostics is going to speed up in the next few years, because their is more and more data that is going to become a major input into AI platforms and they can learn, we can learn, and look for platforms like Processing of Language and Analytics of Medicine, etc., that will allow us to see, and teach these platforms to perform tasks that humans do. X-Rays are a good example of diagnostics.Here was the first historical breakthrough: 20 to 30 years ago, stays were on films,with bad memory drives, and you had to get them processed and printed out to view them. The pixels were also very bad quality, which gives the painter it a negative start to their diagnostics. And comparing it to now and our time, we have advanced technologies that perform these amazing tasks,and AI will get up to that even, and center all of the other 3rd party hardware and software establishments that we use currently to gather the information, but the problem is that they are all on separate platforms. If these components were part of one of the main platforms of AI, we can futuristically have a great way of diagnostic get problems to AI in Healthcare towards the future.
  • Neural Networks drive the diagnosing, and will improve in the future and be able to process more RAM (Random Access Memory) data in the future as our technologies improve.
• How will AI have an impact on patients in the future?
• Sun Microsystems AI predicted is going to replace most doctors in the that is the way that the future is going to continue in their future, but Pooja doesn’t think that tat will happen. Machines she thinks should be a companion to humans in the future, and we should collaborate our minds and not act like it is slavery. This should be a beneficial aspect of AI collaborating with humans.

I found all of these examples very interesting and informative, and I was able to better base my research. Here are some ideas that I got from Pooja about where to futuristically conduct my research:

• Hardware technology powering Neural Networks
• How we can make Hardware smaller and more effective
• Platforms for AI pros and cons
• Natural Language processing Neural Networks and how they contribute to AI in Healthcare

Over all this interview had me realize the importance of us wrong with these technologies because of the massive amount of data that these machines can discover out of the ordinary thing that we can’t.