Sunday, January 20, 2019

How Artificial Intelligence is Affecting Medical Science?

Artificial Intelligence in Medical Science

It’s likely that machines will be smarter than us before the end of the century—not just at chess or trivia questions but at just about everything, from mathematics and engineering to science and medicine.” 

- Gary Marcus



Advancement with Science and Technology has brought to a different dimension which one would have never imagined when there was time tussle going in world arena leading to World Wars. Any severe disease meant that there will be upcoming sobering among the family members. Consistent practices were deployed in order to find alternatives and solutions. But in it was the pessimistic and persistent efforts to gain something, we reached this juncture where we can say proudly that engineering mixed with the medical platform has expressed a variety of answers to those diseases.

Some of the diseases which we have never heard have created complicated situations in the family. Recent in the news, a mobile app has arrived with voice assistance for throat cancer patients. These patients voice box has been taken out in order to stop the spread of tumor and affected area in the human body. One will observe that the detection of some of these diseases has been possible rapidly just because of the growth of Artificial Intelligence and it’s and practical application in the real world, especially in medicine.
If one ascribes to the theory that humans became the dominant species on Earth largely due to having the highest intellectual abilities, then what happens when Artificial Intelligence (AI) exceeds that of human intelligence? If the technological singularity is achieved, will it be the inflection point that will ultimately lead to the improvement of the human condition? Or is there reason to fear that AI will lead to a dystopian future with artificially intelligent robots and machines running amok as fictionalized by films such as The MatrixBlade RunnerThe Terminator, and 2001: A Space Odyssey, as well as the science-fiction television series Westworld?
Also read: Trending Technologies in 2019


Eye, Lung, Radiopathy:

One has many aspects to cover in the medical field:

  • Diagnosis
  • Systematic Operations
  • Medical Reporting
While modeling is an improvement over standard documentation, artificial intelligence (AI) technology is evolving at a very rapid rate. The ability of AI technology to augment decision-making processes is attributed to the speed of pattern recognition and the robust amount of data that are digested and analyzed for optimal health outcomes. AI systems are slowly learning to diagnose the disease as well as any human doctor, and they could soon be working in a hospital near you. The latest example is from London, where researchers from Google’s DeepMind subsidiary, UCL, and Moorfields Eye Hospital have used deep learning to create software that identifies dozens of common eye diseases from 3D scans and then recommends the patient for treatment. Internet giant Google says it is considering how it can use Artificial Intelligence (AI) to help doctors in Africa tackle some of the continent’s most pervasive diseases. Many Companies are considering to start a pilot project with Google’s new AI research center in Ghana, though Google would first “get a better understanding” of the types of diseases it could target on the continent.
According to the World Health Organization (WHO), there were about 212-million malaria cases and 429,000 deaths in 2015 – with more than 90% of those in sub-Saharan Africa.
AI would ultimately make healthcare more accessible, would allow doctors to spend more time with patients rather than on paperwork, and would detect new types of diseases by identifying patterns that humans could not. A new artificial intelligence algorithm can reliably screen chest X-rays for more than a dozen types of disease, and it does so in less time than it takes to read this sentence, according to a new study led by Stanford University researchers. Scientists trained the algorithm to detect 14 different pathologies: For 10 diseases, the algorithm performed just as well as radiologists; for three, it underperformed compared with radiologists; and for one, the algorithm outdid the experts.
Also read: What is NLP?


Diabetes, Cancer, Skin & Master Brain:

Well, this could have discussed under the eye section but it will create more impact when we discuss the diagnosis by AI under the flagship of diabetes. AI tools have been created for handling Diabetic Retinopathy which was hard to discover firstly and then hard to monitor but with AI one of hindrance has been removed. Coming to the skin, Staphylococcus Epidermidis Infection has now been part of AI’s easiness to detect such issues in a smooth manner. Cervical cancer has come into the domain of AI. And last but not the least, AI can tap Alzheimer’s Disease pretty which was next to impossible to detect in short span. This with prolonged time brought a grave situation. AI using the glutathione and hippocampus has arrived in the situation to derive the current status of this disease so that pre-medical remedies can be enacted in order to stop further growth.

Exploiting Electronic Health Record Data:

At present, a simple step such as utilizing electronic health-record data—with patient identifiers removed—is a potentially helpful resource to monitor infectious disease outcomes, vaccine uptake, and adverse drug reactions. These technologies are now widely deployed throughout the healthcare sector, and they provide an opportunity to facilitate change in the way infectious diseases are detected, and potentially diagnosed and treated. AI provides additional information using the most up-to-date research from around the world and includes outcomes figures, allowing immediate responses or modifications to an emerging infectious disease outbreak. This analysis can occur quickly and efficiently and can also assist in warning of any emerging infections not currently known to exist in a geographic region. The technologies are now being embedded in portable devices, such as cellular phones, for easy access to clinical data at any time in the healthcare process. AI data that are cloud-based can be sent to e-mail lists, electronic drug databases, and search engines that allow focused clinical questions and answers in real time, with accuracy based on numerous pieces of data accumulated, dissected and stored for immediate use.
Major vendors, such as Microsoft, using AI are partnering with other vendors (e.g., Adaptive Biotechnologies) to decode the human immune system with the goal of leveraging the company’s “machine learning and cloud computing capabilities” to assist in the bioinformatics analysis of DNA sequencing data of T-cell and B-cell receptors, which make up the immune system. Once data are accumulated, AI processes the information to develop a “universal T-cell receptor/antigen map” to detect disease.  IBM’s use of AI supporting healthcare professionals and various other healthcare stakeholders to improve the care and health outcomes of who have or are susceptible to infectious diseases. IBM’s Watson supercomputer software has the ability to unlock insights using a vast array of data. This, in turn, permits prompt actions to manage the disease with best practices as determined by AI, thereby complementing the clinician. In addition, AI solutions provide data integration and aggregation, creating prevention strategies for high-risk patients as well as for the general population.

Conclusion:

AI systems can be extremely powerful and if used correctly, very supportive of current healthcare and its growth. However, one element that is often not emphasized enough is the role played by data in any successful AI-supported system. Datasets will prove invaluable as they are collected from the various electronic and social media feeds. Additionally, electronic systems have collected and analyzed numerous publications, all of which are combined to create a diverse database of information for us to learn from and inform healthcare decisions.



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