Category: Analytics

Applications of Artificial Intelligence in Health Care

According to experts, a day will come when computers would be used to do most of the jobs, and healthcare is not an exception. Even in today’s world, computers, and machines are used in almost every walk of life. Advanced applications of technology featuring machine learning, artificial intelligence and automation have greatly affected the medical industry including hospitals and insurance companies. The impact is of a more positive nature when compared to other industries. 86% of companies are using AI in health care, including life science companies, and healthcare companies. It is estimated that these companies could spend upwards of $54 million by 2020 on AI.
Artificial intelligence in medicine can and is being used to manage records and miscellaneous data. AI can be used to re-format, trace, and store data which will provide faster access. This type of data management is one of the most widespread uses of AI in healthcare.
Also Read: 4 Industries Where Artificial Intelligence is Making a Huge Impact
It is also used to do repetitive jobs like analysing X-rays, CT scans and various other tests, along with routine tasks. These tasks can be done faster by machines than humans, as there is little to no variation in the pattern of the job.

As one of the main advantages of artificial intelligence is analysing data, things like reports and notes of a patients file, clinical expertise, and externally conducted research can be analysed by AI to select the best way to treat a patient. This can open up opportunities for customised treatments for individuals which might have been difficult in the past due to insufficient data management. In layman’s terms, AI can be used for treatment designs.
Digital consultation is also possible using artificial intelligence. There is an app called Babylon which is based in the UK and it uses AI for medical consultations. Common medical knowledge along with patients’ personal medical history is used to offer recommended actions. Symptoms are reported to the app, and the app uses speech recognition technology to compare the symptoms to a database of diseases, and illnesses.
Rather new and quite unheard-of use of artificial intelligence in health care are virtual nurses. This is mainly used by a start-up company Sense.ly, which developed Molly – the virtual nurse. The program of this virtual nurse employs machine learning to help patients by following up on treatments, and what to do with doctor’s visits.
Medication management can also be streamlined by using artificial intelligence. AiCure is an app developed by the National Institutes of Health, which is used to monitor the health of a patient. The app uses the front camera of smartphones to check if the patients are taking their medicines regularly.
AI can be of great use in developing new medicines. The primary way of making new medicine is to have clinical trials which take years and cost a lot of money. With the use of AI, the entire process can be completed much faster. Recently, during the Ebola outbreak, AI was used to scan medicines and find a way to redesign them to fight the disease.
AI can be used for precision medicine, which can be used to inform people of their potential health risks before they even happen. Genetics can be used along with AI scans of the body to spot eventual cancer and vascular diseases. Mutations can also be predicted by using AI in health care.
Digitising the healthcare system would make accessing data faster, and save a lot of valuable time. The best example of using AI in such a way can be found in the Netherlands, where 97% of all healthcare bills are digital. This can be used later on to create a treatment chart, and can also be used to find out any mistakes during treatment, and inefficiencies in the workflow.
Related Article:

• How is Artificial Intelligence Transforming Healthcare
• 10 Interesting Facts about Artificial Intelligence
• The Major Industries that Artificial Intelligence will Transform by the Year 2020

Technology is transfiguring itself at a brisk pace and amelioration is the only constant presence in the technological forefront. Business in the modern era is abetted via evolved tools and software’s and the life cycle of current practice in any business is diminishing by the dominion of even newer and evolved apparatus as they plummet in the cooperative circumference. Amidst all these transformed disruptive features there exists an entity that outsmarts in utility and efficacy by embellishing itself as the most valued asset of any organization generally known as Information. Information is the torchbearer of the inflecting dimensions in business which succours in establishing reforms for profitability. Information finds its exertion in the domain of forecasting and caricaturing an informed decision for the company’s subjects. Mining of information is articulated through large chunks of relevant data sets organized in any paradigm possible.
Also Read: What’s Big Data All About?
Big Data is one such bank and repository of immense data sets which requires a fine tuning and segregation to infer a surplus germinating through it. Apprehending, Garnering and finally Analysis of data sets are the eminent strides incorporated while deciphering Information. The traits and attributes incorporated in Big Data can be assimilated through the impending titles as follows:

Volume

Proportionality of refined information gets augment and amplified with the abundance of data present in the company. Business is accompanied in a more data-drove structure thus garnering the data from various sources such that a chunk of relatable bundles of data is sprouted which has an inherent ingenuity to extract potential insights and inferences which ensures the longevity of business in a thriving perspective.

Velocity

Rate of production of data is one of the most beguiling tasks arbitrated in the process and velocity at which the data is accumulated and processed for further discretion determines the potential of the company in the technological evolved forefront. Velocity also concerns itself with certain amendments and modifications related to data and more importantly pace at which the same is conducted so that an intuitive deduction is established for proceedings to follow in the industry.

Variety

Data accumulated and cumulated in the company’s credentials can irrevocably be considered as a valued asset. However, the value certainly gets escalated when the diversity and heterogeneity are incorporated in the data sets. As data is accumulated while referencing through various sources and spots, a sense of authenticity gets exhibited which ensures a differentiated yet potent data set that can be mined to revive some fruitful outcomes. The data sets accumulated from patent sources can sustain in various formats such as

• Structured Data Format: – The orientation of this data set is at the epitome of the organized structural spectrum where data sets are exhibited and portrayed in a user-friendly alignment which directly ensures segregation of useful data from various irrelevant data sets and the computing time to harness such information gets mitigated. The Structured format may also exist in the form of a Matrix structure comprising of rows and columns where credible data is easily maculated from the rest of the flock.
• Semi-Structured Data Format: – The Orientation exhibited is in the form of organized paragraphs where the useful inferences are drawn out through precise revision of each data set in the array.
• Unstructured Data Format: -No orientation is disseminated in the alignment and the data is dispersed comprising of videos, images and texts in the same structure.

Variability

With large data sets comes the inconsistency and issues of authenticity which needs to be constrained and bracketed in a closed sphere for an efficient and effective outcome through mining.
Related Article:

• What is Big Data and Business Analytics?
• Job Roles and Responsibilities in Big Data
• The Role of Big Data in Digital Marketing

Artificial Intelligence is a techno beast which is out of the cage and willing to strike big. It was an idea whose time came when IBM powered Deep Blue computer forced Grandmaster Garry Kasparov into defeat. Even though we didn’t know much about AI back then, largely because it was mostly under the supervision and not ready for common use. But now is the time to take a deep plunge into AI if you want your businesses to progress in today’s highly competitive world.
For any business, consumer data is very important and the fine details of this data predict your product’s success in the market. Even though it looks very simple but deciphering consumer data is not an easy task by any stretch of the imagination. From the surface, it can look either positive or negative, where a layman’s approach would be to sift the positive out of it and focus on it to feel happy about the prospects. Most companies don’t even look at the major part of the data available to them because it is very complex. And that is why most companies remain average. Forrester Report 2014 states that companies only look at 12% of the total data available to them. This is tantamount to a blunder if you are doing it out of oblivion.
Also Read – 4 Industries Where Artificial Intelligence is Making a Huge Impact
Data analysis is mostly done in an inept manner because it is done by content marketers who aren’t equipped to handle data analysis. Big data is not possible to be handled by content marketers. That is where Artificial Intelligence comes into the picture and not only brings the horse to the well but makes it drink too. Here are a few basic things AI can help you with.

AI helps in structuring and comprehending the data in a better way. It not only analyses and sifts the significant pointers but also builds prospective leads. Harvard Business Review says that Conversica AI is one such example which is so good that it gave juicy leads to companies.

• AI makes your marketing personalized as it is adept at understanding the consumers’ patterns, interests and present needs. Such a fine-tuned analysis cannot be done without the help of Artificial Intelligence. One can float a survey and find out that the majority of the customers would be willing to buy your product if it caters to their interests and how they want it. A personalized marketing can even help high-end products sell like hotcakes. Everybody likes to be pampered.
• AI erases any chance of manual tottering with the complex set of data. Once you reached the zenith of manual sifting, AI comes into play and gathers, arranges, dissects and presents to you the simplest of the data pointers which anyone adept at data analysis would understand. All companies would like to have more time at hand to make better strategies and campaigns instead of sitting with the data and chewing on it. By the time you are manually done, the consumer dynamics would change.

Your consumer data is your intersecting point with your presence as well as prospective consumers. Companies have a good chance of making a good dough of it and create great communication, products and services which will take their revenues to another level. This is why you need Artificial Intelligence.
Trust Artificial Intelligence to come to your rescue when the Big Data is involved. Ignoring it is not an option. If it can beat Kasparov in the 80s, the absence of it would mean you can be beaten as well. Companies aren’t Kasparov, his points weren’t deducted, yours will be.  We hope that businesses will make a good choice.
Related Article:

• 10 Interesting Facts about Artificial Intelligence
• How is Artificial Intelligence Transforming Healthcare
• Artificial Intelligence in Investment Banking – The Technological Revolution

A trend on twitter is any hashtag-driven topic that gets popular within a period of time. It is any marketing departments dream come true if their hashtag starts trending or in other words gets popular on Twitter. Did you ever ponder over the fact on how these trends on twitter get started? Who or what chooses which stories should appear in the trending section, and why others get left out. Also ever wondered twitter trends are tailored. What you might see as trending might not encompass all that is trending in the world or even in your area. Trends are determined by an algorithm and are tailored to an individual, based on what they like, or who they follow, location etc…, although there would still be some topics which will appear in the trends section in spite of personalisation.
There are algorithms of twitter that are responsible for hashtags trending. These algorithms determine what is trending by observing and favouring spikes over any gradual sustained growth. For any topic to trend would need a combination of volume, the time taken to create that volume. So to put it simply, if a hashtag gains volume in one day it is considered as trending, but at the same time if the volume takes 30 days to accumulate, then it will be considered as news in general and not a spike.
If you want a hashtag created by you to trend, there are certain factors that can be practised to add to the volume. Be creative with the name of the hashtag, keep it simple with one objective, for example, if you want to create a Hashtag you need to drive one message, #standwithIndia is a better example than #standwithIndiancitizens.
Next thing is to make sure you get more than 500 tweets within the first hour, a point to remember is that not only the number of tweets is important, but it is equally important on how many individuals are tweeting.
To elaborate, common hashtags like #cat, #food, #music are very common and are tweeted every day, hence such hashtags might never start trending, however now if a totally different set of people start using these hashtags then there is a high possibility that they start trending.
So to conclude there are many factors that could push your hashtag trending on Twitter. It is Twitter’s trending algorithm eventually that finds topics that many people start tweeting at once. Like when there is noise about a particular launch of an album, television show, a natural calamity like an earthquake or a tsunami, or a death of a famous personality. At the same time, if a lot of people start tweeting about rain, it will never start trending as it is a common topic.
According to a study by HP labs, 31% of trending topics comes from retweets. 72% of those originate from 20 major news sources which are mostly based in the United States and the UK.
Like explained before, hashtags start trending if they haven’t been through the algorithm before, they might trend, then go off the grid and later start trending again if a totally different set of users discover the topic and start tweeting about it. The trending might not appear more than 40 minutes at a time, however, the trends on Twitter are considered as such prime spots for the marketing teams in general that a promoted space at the top runs at $120,000 per day.
Interesting thought!
To learn more about twitter trending algorithm watch this space until next week for the big news!

Business Analytics refers to the practice of investigation of past business performance using data and statistical models in order to develop new insights and understanding of future business performance. Business analytics makes extensive use of statistical and quantitative analysis, explanatory and predictive modelling and fact-based management to drive decision-making.

Business Analytics is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and an organizational commitment to data-driven decision making.

Also Read: What is Business Analytics?
Examples of Business Analytics uses include:

• Exploring data to find new patterns and relationships. (data mining)
• Explaining why a certain result occurred. (statistical analysis, quantitative analysis)
• Experimenting to test previous decisions. (A/B testing,
• multivariate testing)
• Forecasting future results. (predictive modelling, predictive analytics)

Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis.  Data acquisition often involves extraction from one or more business systems, cleansing, and integration into a single repository such as a data warehouse or data mart.

The analysis is typically performed against a smaller sample set of data.  Analytic tools range from spreadsheets with statistical functions to complex data mining and predictive modelling applications.

As patterns and relationships in the data are uncovered, new questions are asked and the analytics process iterates until the business goal is met.

Deployment of predictive models involves scoring data records (typically in a database) and using the scores to optimize real-time decisions within applications and business processes. Business Analytics also supports tactical decision making in response to unforeseen events, and in many cases, the decision making is automated to support real-time responses.

While the terms business intelligence and business analytics are often used interchangeably, there are some key differences:

BI vs BA	Business Intelligence	Business Analytics
Answers the questions:	What happened? When? Who? How many?	Why did it happen? Will it happen again? What will happen if we change? What else does the data tell us that never thought to ask?
Includes:	Reporting (KPIs, metrics)Automated Monitoring/Alerting (thresholds)Dashboards, ScorecardsOLAP (Cubes, Slice & Dice, Drilling)Ad hoc query	Statistical/Quantitative Analysis, Data Mining, Predictive Modeling, Multivariate Testing

Recognizing the growing popularity of business analytics, business intelligence application vendors are including some Business Analytics functionality in their products.

More recently, data warehouse appliance vendors have started to embed Business Analytics functionality within the appliance. Major enterprise system vendors are also embedding analytics, and the trend towards putting more analytics into memory is expected to shorten the time between a business event and decision/response.

Related Blogs:

• What is The Meaning of Business Analytics?
• What Are The Benefits Of Business Analytics?

Here is the package of the most popular Big Data interview question you must be prepared with.

1. What do you understand by “Big Data”?

Answer: Big Data comprises of huge chunks of data which cannot be handled by modern day computers and requires new frameworks.

2. What are the sources for Big Data generation in case of IoT?

Answer: Sensors are the most common source in the case of IoT.

3. What are the five V’s of Big Data stand for?

Answer: Volume, Velocity, Variety, Veracity and Value.

4. How would you define “Hadoop”?

Answer: Hadoop is a set of frameworks which are used to process big data using parallel computing.

5. Name any core components of Hadoop?

Answer:

• HDFS for storage
• MapReduce/YARN for processing.

6. What is the need to have Hadoop?

Answer: Hadoop is required for scalability. It is easy to build solutions for a particular volume of data. However, solutions for increasing amount of data are complex.

7. In what ways big data and Hadoop are related?

Answer: Big Data and Hadoop go hand in hand. Without Big Data there is no Hadoop and without Hadoop, there is almost no way to process Big Data. Hadoop is the gateway for all other applications to be modelled for Big Data.

8. How does Apache Hadoop resolve the challenge of big data storage?

Hadoop has its own file system, HDFS. It tries to solve all ends of data storage. Firstly, it is schema-less in nature and highly compressed. It is stored as binary. The file system also maintains redundancy so that there is data reliability even when a machine fails.

9. How can big data analysis help in revenue generation?

Answer: Big Data analysis can improve revenue for any company. For finance companies, it can help in crunching down huge amounts of financial data to find loopholes and mistakes. For healthcare, it can be used to detect problems with patients through patient history and digging the vitals of all the historical patients. Similarly, any kind of company can use big data analytics to correct financial leaks.

10. Mention some of the fields where you can apply Hadoop?

Answer: Hadoop can be applied at any place where big data exists. It can be used to:

1. Model traffic
2. Model a high-frequency trading platform
3. Graphics rendering

11. What are the differences between Hadoop and Spark?

Answer: Hadoop has its own storage system whereas Spark doesn’t. Spark is generally faster than Hadoop but the latter is more reliable and has much more support and tools available.

12. In how many modes one can run Hadoop?

Answer: The Hadoop daemon can run on three different modes: Standalone, Pseudo-distributed and Fully distributed.

13. Name the common input formats in Hadoop?

Answer: The commonly used input formats in Hadoop are: Key-Value mapping, Plain text and Sequence file input.

14. Define HDFS?

Answer: Hadoop Distributed File Systems (HDFS) is the core Storage solution for Hadoop. It is a wrapper on the commonly used Linux filesystems such as ext3, ext4, etc.

15. Name the components of HDFS.

Answer: The two main components of HDFS are Name Node and Data/Slave Node.

16. What is meant by FSCK?

Answer: FSCK stands for File System Check. It is used by HDFS to check for any missing blocks or corruption in data.

17. What do you understand by DataNode?

Answer: A Data Node is a slave node. It is the one which stores data and has a linkage to Name Node.

18. Mention the functions of NameNode?

Answer: NameNode holds any kind of metadata for the data. It acts as Master for managing the Slave Nodes.

19. How Is DataNode failure tackled using NameNode?

Answer: The NameNode is responsible for tackling replication of data. Hence, it keeps track of each DataNode replication and hence handles a DataNode failure.

20. Is there any problem in using small files in Hadoop?

Answer: Yes. Hadoop is not made for small files. The default block size for HDFS is 128 MB. Anything smaller than that reduces hit rate and makes the program slow.

21. How can you resolve the issue of small files in Hadoop?

Answer: HAR (Hadoop Archive) has been built as a wrapper on HDFS to tackle small files.

22. What are the salient features of Pseudo Mode?

Answer: Pseudo mode simulates the environment of a parallel machine. During testing, one would want to see how their program will work on bigger data with more threads. The pseudo mode is for that.

23. How can one achieve security in Hadoop?

Answer: Kerberos is the de facto for any kind of Authentication in Hadoop.

24. State any two limitations associated with Hadoop?

Answer: Two limitations of Hadoop are:

• Does not handle small files well.
• Processing speed is low due to heavy map and reduces operations

1. Mention the role of Job Tracker in Hadoop?

Answer: Job tracker manages resources. It makes sure that there is load balancing and no node is becoming the bottleneck.

26. How one can debug Hadoop code?

Answer: Hadoop logs every step. The logs can be found in the installation directory.

27. How does Reduce Side Join differ from Map Side Join?

Answer: The data needs to be structured for a map-side join. Not the case with reduce side join.

28. Mention one difference between Input Split and HDFS Block?

Answer: Input Split is done during mapping operation for nodes and is not permanent. HDFS block is a permanent storage solution.

29. What do you know about rack-aware replica placement policy?

Answer: Sometimes, a node’s physical location becomes important during allocation. Hence, rack-aware replica placement policy keeps track on that.

30. What is the function of a DataNode block scanner?

Answer: Block scanner looks for any corrupted DataNodes and reports them.

31. What do you understand by MapReduce?

Answer: MapReduce is the heart of Hadoop. It splits data into appropriately sized chunks and allocated them to nodes accordingly.

32. Mention two salient features of MapReduce?

Answer:

1. A robust and tested architecture for data distribution and parallelization.
2. Achieves good load balancing and scalability.

33. Enlist the components of the MapReduce framework.

Answer: There are four components of the MapReduce framework:

1. ResourceManager
2. NodeManager
3. Container
4. ApplicationManager

34. What is InputFormat?

Answer: It defines the input format and configuration for an MR job.

35. When do we use jps command in Hadoop?

Answer: It is used to check if the Hadoop daemon is up and running.

36. What is meant by Speculative Execution?

Answer: Not all machines in a cluster might be the same (or give equal performance). Hadoop runs the same instance of MR jobs on multiple machines in case some machine is performing poorly.

37. Mention the steps involved in the NameNode recovery process?

Answer: First a new NameNode is created. It is then connected to DataNode and Clients and acknowledged. In the final stage, the NameNode starts serving the client and receives block reports from DataNodes.
 

Most businesses today encounter large amounts of data which can be processed and analyzed to see trends and improve techniques. Smaller transactions can be handled through excel sheets or other similar software, but as the data starts compounding, it’s time to think of big data and its analysis.
Big data is all around and will not fade away anytime soon, Thus, it becomes important to break it down into 5 V’s which properly describe it.

Velocity

Obviously, velocity refers to the speed at which this huge data set is being generated, collected and analyzed. According to a research, every day almost 900 million photos are uploaded on Facebook, 0.4 million hours of video uploaded on YouTube, 500 million tweets are posted on Twitter and a whopping 3.5 billion searches are performed on Google. Imagine where all this data goes, and how these platforms are still able to perform their tasks so efficiently. Every second, the amount of data is increasing, and big data methods have to be used. Big data helps these companies to accommodate this inflow, accept it and process it very fast so that there are no glitches.
Also Read : Impact of Big Data on the World

Volume

Volume refers to the incredible amount of data generated each second. To an ordinary person, it may seem like a nuclear explosion of data. There is no sense to focus on minimum storage units because the amount of data is growing exponentially every year. There is data I phones, laptops. Social media platforms, credit cards, photographs, videos. Facebook has 2 billion users, YouTube has1 billion users, Instagram has 700 million users while Twitter has 350 million users. These users continuously add to the amount of data through uploads, Collecting and analyzing this data presents a huge challenge for engineers and data scientists.

Value

When we refer to value, we refer to the net worth of all this data which is present. Having huge amounts of data is great, but unless you can utilize it for your profit, it is useless. As we know that huge sets of data do not correspond to helpful insights always. It needs to be monitored for the key function of your organization. Whether the data can help in launching a new product line, whether it presents a cross-sell opportunity or whether a cross-cutting measure, it has to be figured out. The cost and benefit of big data need to be kept in mind.

Variety

Variety in big data refers to the structured and unstructured data which is generated by machines or humans. Data today is very different from data in the past. 80% of the data today is unstructured and cannot be fit into a table. It includes photos, videos, email, voicemails, handwritten text, social media, etc. There are no rules with unstructured data while structured data has to fit into metamodels. Variety is all about being able to classify all this data into categories which can be stored, analyzed and used simultaneously.

Veracity

Veracity refers to the quality or trustworthiness of data. It basically tells us whether the data is accurate or not for us. This is one of the disadvantages of big data and there are always some discrepancies which might be present. As the above-mentioned properties increase, the veracity of big data decreases. It determines more the meaningfulness and the reliability of data. Think of social media posts with hashtags, typing errors, abbreviations, etc. They increase the bulk but decrease the quality of data. The knowledge of the veracity of data can help in understanding the risks associated with future business plans and prevent past mistakes.
Related Article:

• Job Roles and Responsibilities in Big Data
• The Role of Big Data in Digital Marketing

Artificial Intelligence is swiftly changing the way the healthcare industry operates. Several breakthrough innovations have allowed AI systems to become a more stable presence throughout the healthcare industry steadily. Initially, when introduced, AI was used only to manage data in areas like medical records and medication history. Today, AI is now being used in a broader spectrum of healthcare management. It is being used to perform radiological tests like X-rays and CT scans and is also used to help design treatment plans for patients. Artificial Intelligence has been used with digital nurses such as Molly who performs all tasks that a regular nurse can. Research is also being done to see if AI-led 3D printing organs and bones will be more suited as prosthetics.

The growth of AI has been rapid, and some hospitals have begun experimenting with it in precision surgical programs. Robotic surgeries are quickly gaining a reputation for being less invasive and far more accurate. Non-invasive surgeries mean the body is not invaded, i.e., it is not cut open during surgical investigations.
With the rapid development in AI, there are immense possibilities when it comes to surgical procedures,

• Increasing the accuracy of complex surgeries, for example,  locating a bleeding point.
• Reducing the number of invasive procedures.
• More precise calculation for the point of incision and angle of the incision to reduce the recovery period.
• Rapid reaction AI to react to and combat changes in the body like a spike in blood pressure.

The potential for AI-only surgeries is already being discussed and researched reducing the dependency on available surgeons. Currently, diagnosis is too reliant on the diagnostician’s skill. This means that the diagnosis and care of a patient are subject to human error. With the introduction of standardized AI, the pressure of diagnosis is removed from the medical practitioner. A more accurate diagnosis is derived since AI relies more on data than diagnostic skill and approach. Now concentration is more on providing added comprehensive care for the patient.

Artificial Intelligence is also evolving from within. As more data is accumulated from systems like Molly, the diagnosis speed and treatment accuracy will increase substantially. Machine learning is going to change the way these systems operate. Not only will the industry see an increase in efficiency, but these systems can also be used for pharmaceutical needs like drug creation or diagnosis and healthcare management needs.
AI can be of use in homes as well as in hospitals. With smart monitoring devices already entering the market, some AI devices are being used to monitor blood pressure and blood sugar levels, body temperature and other common diagnosing factors for real-time results.
Robotic and surgical arms like the Da Vinci surgical arm have already made an impact in the field of complex surgical procedures, by taking care of intuitive tasks in prostate surgeries and hysterectomies. In time AI will be a significant player in processes that will help surgeons with cardiovascular surgeries as well.
At a non-medical view, AI is allowing doctors to access medical files quicker and cross-reference them more accurately. It is improbable to think that to access medical history, a person had to rummage through a massive record room. Now, with the digitisation of the industry, everything is a click away. Blood reports would need a sample to be sent to an external lab for diagnosis where a technician would take a few days to analyse the blood manually before sending the findings across. Now, through digitisation and automation, a lot of the detection can be done within hours and attached to the medical files of the patient and be available immediately to the doctor.
AI is still in its infancy in the healthcare industry. The industry itself is filled with numerous risks and pitfalls and the potential for fatalities is still higher for patients than it is for users in any other industry. In a short amount of time, AI has made a difference and has already reduced the invasiveness of surgeries. In time, it can be the revolution that makes this risky industry safe and secure. With AI playing a bigger role in all aspects of healthcare, expect fatalities to drop, precision medicine to act quicker and expect the entire healthcare industry to change in the foreseeable future.
It is fair to say that as AI develops, you can expect to see innovations in record management, diagnosis speed, accuracy, the risk involved in complex procedures, lab testing, drug creation and treatment management.