DEV Community: Swathi Krishna

Enhancing Your Supply Chain Management: The Top 5 Benefits of SAP S/4HANA

Swathi Krishna — Tue, 18 Jul 2023 07:30:54 +0000

*Key Features and Capabilities that Make SAP S/4HANA Shine
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Real-Time Insights: SAP S/4HANA processes massive amounts of data in real-time, empowering businesses to respond swiftly to market dynamics, identify emerging trends, and seize opportunities before they fade away. It's like having a nimble and swift chariot that propels your business forward, helping you stay ahead of the competition.

Enthralling User Experience: With an intuitive interface and personalized dashboards, SAP S/4HANA transforms ERP navigation into a delightful journey. It's like stepping into a realm of effortless elegance, where tasks are completed with grace and efficiency, unlocking your teams' full potential and driving productivity to new heights.

Simplified Data Model: Bid farewell to complexity and data redundancies. SAP S/4HANA enables you to declutter your business landscape, focusing on innovation and growth. With a simplified data model, your organization gains agility, flexibility, and the ability to adapt swiftly to changing business needs.

*Preparing for SAP S/4HANA Implementation
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To embark on an SAP S/4HANA implementation journey with confidence, organizations must lay the right foundations:

Assessing Business Readiness: Evaluate data readiness to ensure information accuracy, completeness, and compatibility with the new system. Allocate resources appropriately, including skilled personnel and budget, to navigate the implementation journey smoothly.

Building the Implementation Team: Assemble a cross-functional team, including project managers, functional consultants, and technical experts, to bring together diverse perspectives and address all aspects of the implementation comprehensively.

Charting Your Course: Define clear goals and scope for the implementation to provide direction and prevent aimless wandering. Prioritize areas that will benefit most from the new system, optimizing time and resources for effective project planning and success.

*The Six Phases of SAP S/4HANA Implementation
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Each phase of the SAP S/4HANA implementation process brings your organization closer to harnessing its full potential and unlocking new levels of efficiency, agility, and competitive advantage:

Project Planning and Preparation: Create a detailed project plan, establish timelines, and define deliverables. Effective stakeholder communication and change management strategies ensure a structured and well-organized implementation process.

System Installation and Configuration: Assess hardware requirements, design the system landscape, and install and configure the SAP S/4HANA system. Ensure optimal performance and integration with other systems.

Data Migration and Conversion: Extract, transform, and load legacy data into the SAP S/4HANA system, ensuring accuracy and data integrity.

Customization and Configuration: Set up master data, define workflows, and configure system functionalities to align SAP S/4HANA with your organization's unique processes.

Testing and Training: Thoroughly test the system's functionality, performance, and data accuracy. Train end-users to effectively utilize SAP services.

Go-Live and Post-Implementation Support: Launch the system and provide ongoing support. Implement continuous improvement initiatives and enhancements based on user feedback and evolving business needs.

Overcoming Common Challenges with SAP S/4HANA Implementation

Data migration complexities, resistance to change, resource constraints, and technical integration issues are common challenges during SAP S/4HANA implementations. Organizations can overcome these challenges by engaging experienced consultants, investing in data cleansing and migration tools, prioritizing change management, allocating sufficient resources and budget, and collaborating closely with SAP experts.

*Partnering with Fingent for SAP S/4HANA Implementation
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Fingent offers comprehensive services to deploy SAP S/4HANA for superior supply chain management. Our consulting services assess your supply chain management requirements and design a customized SAP S/4HANA implementation strategy. With our expertise in SAP S/4HANA and supply chain management, we guide you through the entire deployment process, from strategy and implementation to ongoing support, unlocking the full potential of this transformative platform.

Influence of Medical Chatbots in the Healthcare Sector

Swathi Krishna — Fri, 29 Oct 2021 06:52:49 +0000

All healthcare providers are always ready to help their patients and understand the importance of being available when urgent medical care is needed. Unfortunately, doctors have a limited amount of time and several requirements prevent them from tackling all of them. Chatbots can play a wonderful role in helping these patients and guiding them with authentic and trustworthy information.

Medical chatbots are critical to advancing efficient and accurate patient care. They are also expected to support the global need to create affordable and sustainable healthcare for all people on the planet. Today, healthcare chatbots even offer some surprising benefits, such as automating the tedious healthcare process, caring for patients 24 hours a day, 7 days a week, and improving management areas such as payments, customer service, and marketing.

Medical Chatbots
A medical chatbot is a powerful AI application that is widely used today to improve patient care. Additionally, these bots can improve the accuracy of disease and symptom detection, as well as prevention, feedback processes, post-recovery targeting, etc. They help to perform everyday tasks like sending emails, providing simple solutions, lead generation, marketing, analyzing results, etc. Since these chatbots can perform simple tasks, healthcare workers and healthcare professionals can focus on more important duties.

Beneficial Features of Chatbots in the Healthcare Industry
Confidentiality
This is one of the most important aspects of the healthcare system, as it is the responsibility of all institutions to ensure their patients feel safe and secure when sharing their data.

This not only benefits the protection of confidential customer data but also enables the pharmaceutical and healthcare industries to support various medical research and drug discovery. This can be achieved by:
Large investments in chatbot data security
Giving patients a choice in data sharing
Detailed information on the fundamentals of data exchange

Monitoring
Another key feature that chatbots can improve is the ability to track, evaluate, and educate users on health changes, such as changes in physical activity and weight, mental health monitoring, and so on. In addition, these processes are fully automated and are updated each time patients enter new data in their profiles.

In this way, medical personnel can better comprehend and record the health status of each patient and provide information on health checkups and preventive measures to strengthen the immune system.

Customization
If enough information related to the health of each patient is recorded, chatbots can establish better communication with customers and can be used for various purposes. For instance, some chatbot applications can even examine the exact measures of oxygenation, heart rhythm, blood pressure, and body measurements using mobile sensors and also patient behavior using facial recognition.

Interaction in Real-Time
This feature entails reducing the amount of time it takes to react to a patient's request and complete basic administrative duties, such as booking an appointment. Furthermore, real-time interaction includes sending various notifications and reminders to patients, which is far more time-efficient than sending personal emails manually.

Scalability
Last but not least, a key feature of chatbots in healthcare is their ability to engage with multiple patients at the same time. Unlike traditional one-on-one personal online consultations, emails, or phone calls, chatbot healthcare technology enables simple and effective communication optimization between healthcare institutions and their patients.

Advantages of the Health Chatbots
Reduced response time
Management of an unlimited number of users
Medical guidance at the right time
Regular messages and notifications
Significant cost savings
Billing and payment systems that are fully automated
Appointment scheduling

In the healthcare industry, medical chatbots are quickly becoming the new normal. Not only do they communicate seamlessly with patients or end-users, but they also digitize a host of other functionalities in healthcare organizations. These chatbots are extremely smart, and with human assistance, they can help healthcare facilities work smoothly and improve their operations significantly.

Common Web Application Security Threats and Measures to Combat Them

Swathi Krishna — Fri, 30 Jul 2021 06:19:36 +0000

All business owners should recognize and avoid is the existence of software vulnerabilities and threats to web applications. However, regardless of its convenience, there are downsides to using web applications for business activities. To prevent security incidents and their potentially costly damages, business activities must be protected against vulnerabilities in web applications.
Web application vulnerabilities are security flaws that allow cybercriminals to manipulate source code, gain unauthorized access, steal data, or otherwise disrupt the application's normal operation. Many network attacks can be averted if you take a proactive approach to address the most prevalent cyber vulnerabilities and security misconfigurations. The following are some of the most common IT security vulnerabilities and measures to protect your business from these cyber threats.
Injection Flaw
An injection flaw occurs when an attacker uses unfiltered and usually malicious data to attack a database or directory connected to your web application. Two common injection attacks are often used. First, SQL injection is used to attack your database. Second, attacking the directory using LDAP injection.
Injection attacks use input fields that interact with directories and databases to run against vulnerabilities. These include usernames, passwords, and other areas to interact with the target. Due to the lack of input filters during database or catalog development, these fields are often vulnerable to attack.
To prevent this from happening, filter all inputs correctly. This is done by taking advantage of the filtering capabilities of the framework. Protocols such as escape variables can be used to prevent characters used in injection attacks from being sent to modify the directory.
Authentication Errors
Authentication helps applications identify and verify users. Therefore, compromised authentication can allow an attacker to access the target user and have the same permissions as the target user, resulting in severe web application vulnerabilities. Authentication problems can give attackers unrestricted access to your data and cause serious damage to your web applications.
The correct user can be identified using multi-factor authentication. Use regular password updates to create strong passwords to prevent the use of common passwords. Finally, correctly setting the time-out and password strength in the database will avoid authentication problems.
Exposure of Sensitive Data
This vulnerability targets insufficient resource protection. When handling confidential information and data, it must always be encrypted, transmitted over the network, and idle.
Protecting stored confidential data can be a bit difficult, but there are some solutions you can try. One is to reduce exposure; If the data is outdated, don't save it. However, if the stored data is necessary, make sure it is encrypted and the password is hashed. Encrypting the data stored in your database while storing the encryption key separately will reduce exposure.
Missing Function Level Access Control
When server-side authorization is configured incorrectly, damaged, or lost, it will cause vulnerabilities and expose your back-end to attacks. These attacks usually occur on user interface interfaces configured with components so that administrators can access data or other important elements of the application. In this case, most users will not see the management function, but those looking for the vulnerability will be able to discover and exploit this vulnerability through malicious requests.
To resolve this issue, all server-side authentication must be active and configured to prevent unnecessary access.
Misconfiguration of Security
If the application’s security settings, database and web server, framework, and platform are incorrect, an attacker can quickly gain unauthorized access to application data and functions.
Incorrect configuration can be caused by; running outdated software, exposing error handling information, running unwanted services on the machine, etc.
The most effective solution for this is to ensure that the existing architecture has good component separation and security.
Cross-site Scripting (XSS)
Cross-site scripting, also known as XSS, is a vulnerability to scripts running on the user's side, allowing attackers to run scripts in the user's browser. This happens when an application sends untrusted data to the browser without verification. Since the browser does not know the validity of the script, it executes it, allowing an attacker to hijack the session cookie and even redirect the user to a malicious site.
Sanitizing input can be used to mitigate XSS issues. Sanitizing user input will help prevent vulnerabilities from being exploited and injected into websites. Validating and escaping user input will also assist in preventing malicious injection.
Insecure Direct Object Reference
Direct object reference refers to exposing internal files to clients or users. When this happens, all the attacker needs to do is provide recommendations. If authorization is not applied, the attacker will gain access. Through this access, an attacker can make modifications that may compromise the entire application.
Some preventive techniques include the correct and consistent execution of user authorization by implementing access control checks. You should avoid exposing references in URLs as well. Finally, don’t rely on data retrieval through CGI parameters; instead, store data internally.
Cross-Site Request Forgery
Cross-site request forgery (CSRF) uses social engineering to trick authenticated users into clicking links and controlling their sessions. Because the attacker has authenticated sessions, he can change the state of the app instead of stealing data. The attacker takes advantage of the user's access to a certain website and uses it to change the website to which the user is currently logged in.
Using secret tokens or cookies can help verify the real request for malicious requests. In addition, using only POST requests and deleting GET requests can help prevent URL information from being corrupted.
Unauthenticated Redirects and Forwards
An attacker can redirect users to malware websites if sufficient validation is not performed during page redirection. The attacker sends a real URL, but the URL has been appended with a possibly malicious encoded URL. If it is unavoidable, choose not to involve user parameters when determining the destination, or ensure that the provided value is authoritative and valid for your client.
Modern web app development heavily relies on security. A security breach in your web app might result in significant financial losses and damage to your company's reputation.

At Fingent, our experts can provide you advanced security solutions to combat hackers and serve your business with powerful and secure applications to remain competitive in the market. If you are ready to take your safety to a new level, get in touch with us right away.

AI Solutions To Enhance Supply Chain Operations

Swathi Krishna — Thu, 28 Jan 2021 09:08:58 +0000

As the world works double time on managing the coronavirus outbreak, it has also revealed many vulnerabilities in global supply chain operations across most sectors and industries. Today, organizations are in reaction mode. Business leaders are searching for ways to streamline operational efficiency and balance sheets. They are entirely focused on maintaining supply and meeting the needs of their customers through hard work.
Unfortunately, outdated technology is holding the industry back in this time of uncertainty. Inability to adapt to new technology can result in missed or delayed deliveries, closed warehouses, and ultimately, financial loss. Now is the time for leaders to evaluate how they can proactively enhance supply chain operations. This blog shows how AI solutions can improve supply chain operations.
Importance of AI Solution in Supply Chain Operations
Artificial Intelligence (AI) is making its way into all sectors. Interestingly, it is also picking up the pace when it comes to supply chain operations. Custom software solutions are beginning to apply the AI capabilities to detect errors and amplify important decisions.
Procurement of raw materials, managing partners, and planning and executing tasks sequentially requires efficient data analytics. Increasing complexity in supply chain IT infrastructure makes it challenging to achieve the agility and speed needed to survive and thrive in today’s competitive market. AI is essential here.
AI is armed with computing techniques that assist in selecting and analyzing large quantities of data that are collected from the supply chain. It can process terabytes or even petabytes of data. Those results can be used to initiate processes and complex functions.
Uncontrolled fluctuations in demand is another problem that could create more crisis for traditional supply chains to manage. The key would be to find innovative ways to deliver exactly what a customer needs with minimal supply chain interruptions. To increase flexibility and agility in the supply chain, businesses need to focus on implementing AI solutions.
Here are six specific ways AI solutions can enhance supply chain operations:

Machine Learning Can Enhance Supply Chain Planning Supply chain planning plays an important role in supply chain operations. It is thus crucial to have intelligent work tools to help businesses build concrete plans. Machine Learning can help in forecasting inventory, demand, and supply. When it is correctly applied through work tools, Machine Learning can improve agility and optimization of the supply chain. It can thus have a positive effect on decision-making. Supply chain management professionals can use Machine Learning technologies to provide the best possible scenarios. These scenarios are based on intelligent algorithms and analysis of Big Data. This can optimize the delivery of goods while balancing demand and supply without any need for human analysis.
Chatbots Can Enhance Procurement and Customer Support Organizing procurement related tasks need access to robust and intelligent data sets. Chatbots can be utilized in everyday supply chain operations to help with this. They make it easy to: ● Speak to suppliers ● Set and send actions to suppliers regarding compliance materials ● Place purchase requests ● Raise questions regarding procurement functionalities With the help of AI, most customer engagements can be handled by chatbots. This can help in personalizing the relationship between logistics providers and customers.
ML Can Enhance Warehouse Management Supply chain operations depend heavily on proper warehouse and inventory-based management. Supply flaws such as under-stocking or over-stocking can be disastrous for just about any consumer-based company. Additionally, the lockdown across the globe has sent e-commerce into overdrive. Businesses must ensure fast, reliable service to maintain their reputation and obtain a competitive advantage. ML can assist in creating a smart warehouse that is fully automated. That means mundane and manual tasks are simplified and operations become cost-effective. Additionally, ML provides an endless loop of forecasting that keeps improving itself constantly. Such capabilities can reshape warehouse management for the better.
Predictive Analytics Can Enhance Forecasting Supplier selections and sourcing is an increasing concern for businesses. Just one mistake in the operations of a supplier body can prove catastrophic for the business. Merchandisers make critical decisions based on data sets. These data sets include information regarding audits, supplier assessments, and credit scoring. Thankfully, AI can provide the best possible scenario for the supplier section. With the help of intelligible algorithms and ML, passive data set could be activated. Thus, supplier selection would become more predictive and intelligible than ever before. Another area where predictive analytics proved helpful is shipping. Shipping is at the heart of the supply chain. Faster and more accurate shipping can reduce transportation expenses and lead times. As coronavirus is spreading at different rates across the countries, the transportation capacity is reducing. AI services can enhance efficiencies in network planning and predictive demand. It can help merchandisers to become more proactive. When they know exactly what to expect, they can adjust the number of vehicles. This allows them to respond to the demand by directing vehicles to locations where maximum demand is expected. This can minimize operational costs.
Genetic Algorithms Enhance Delivery and Reduce Costs In supply chain operations, every mile and minute matters. Genetic algorithms help companies map out optimal routes for deliveries. It can help drivers make timely and cost-effective deliveries. It allows merchandisers to plan the delivery route depending on traffic conditions or other factors.

Trending AI Software Development Tools

Swathi Krishna — Tue, 28 Jul 2020 13:11:35 +0000

The market for AI technology is flourishing by leaps and bounds. The AI community is so strong, open and helpful that you can find the code, blog or library for almost anything. If not already done, now is the type to make use of AI solutions to meet your business needs.

To build AI solutions, you may have to use various capabilities like machine learning(ML), natural language processing(NLP), deep learning, speech and so on. Such projects require the perfect set of AI software development tools. The expertise of a custom software development company can be leveraged here to utilize these tools and work towards developing path-breaking products. Coming years it is expected that AI tools will play a major role in business.

H2O: H2O is an open-source machine learning and AI platform that permits developers to build and productionize machine learning models. The code can be written using different languages like Python, Java and R. These familiar languages help developers create mobile apps using predictive analysis and machine learning.

H2O comes with a set of supervised, unsupervised and other miscellaneous algorithms that users can utilize. H2O can easily integrate with the tools belonging to the Hadoop ecosystem including Spark. The financial, healthcare, insurance and many other industries use H2O to create ML models and gain better insights.

TensorFlow: TensorFlow is an open-source machine learning library that can be used by experts as well as newbies. TensorFlow can carry out various kinds of intuitive and language understanding. Newbies can just plug the blocks together to build new models. Experts can leverage the define-by-run interface for further analysis. TensorFlow helps to bring new understanding to existing problems and identify new problems that weren’t considered before.

It is one of the best machine learning tools available and developers can do a lot more with TensorFlow. TensorFlow allows APIs in almost all important languages. Since Keras is much easier to execute, you can apply TensorFlow in the backend without the need of being well-versed in it.

Microsoft Azure Machine learning Studio: The AI capabilities of Microsoft Azure is quite impressive. The Microsoft Azure AI platform is one of the most popular choices for AI development. It makes machine learning easier for businesses through advanced analytics. The Microsoft Azure Machine Learning Studio is a collaborative drag-and-drop machine learning tool.

This tool helps you to create, test and implement predictive analytics solutions on your data. You can drag-and-drop datasets and analysis modules, linking them together to launch an experiment. You can run this in the Machine Learning Studio without any programming.

Infosys NIA: Infosys NIA is an enterprise-grade AI platform that simplifies your AI adoption in business and IT. NIA offers best-in-class AI capabilities that empower your enterprise to deliver business value and ROI through data management across sources, use of computer vision to extract information from images or scanned documents and Natural Language Processing (NLP) to understand, interpret and analyze human language. The AI workbench simplifies the creation of machine learning and deep learning models.

NIA also features an ontology toolkit to capture and structure information into semantic representations. Other features include model operations, conversational AI, cognitive search and so on. NIA offers built-in AI solutions to accelerate the time-to-market and drive document digitization. These enterprise-grade features make NIA the preferred solution for enterprises.

IBM Watson: This is another platform that helps data scientists and developers build AI applications. Watson is a cognitive system that processes both structured and unstructured data at massive speed and scale. Watson is primarily an NLP tool that can understand language just like a human. IBM Watson provides tailored solutions for various industries like healthcare, advertising, IoT, media, law and so on.

Watson can identify new insights or patterns locked away in the information. It helps you build AI and machine learning models on any cloud. The pre-built Watson applications give you a faster time-to-value regardless of where you are on your AI journey. Watson APIs such as Watson Speech to Text, Watson Natural Language Understanding, Watson Personality Insights aim to empower your enterprise apps with AI capabilities.

As AI continues to make the world smarter and faster, businesses are looking for ways to put AI to work at scale. The above list of tools and frameworks can help you resolve business problems and be more productive. They enable you to enrich your customer interactions and make recommendations with confidence.

Before embarking on such projects, it is crucial to ensure that your team has the right set of AI and ML skills. Collaborating with a proficient software development team can put you at ease. Fingent’s team of experts can help you unleash the full potential of AI and reinvent your business. Contact us now to know more!

The Underlying Benefits of Big Data in eLearning

Swathi Krishna — Fri, 29 May 2020 12:34:59 +0000

Big data can help the eLearning system evolve to a great degree. Administrators and developers can easily learn about the shortcomings in the eLearning solution with big data and bridge those gaps to make it more efficient.

All Learning Management Systems and delivery mechanisms involved in eLearning are data-driven and collect data as a learner interacts with its modules. This gives valuable insight on how the courses are benefitting the learners. Organizations can also figure out the preferred mode of learning for individuals and groups by analyzing the data. At the same time, it also allows them to understand the strengths and weaknesses of their eLearning strategy.

Key Benefits of Big Data in eLearning
Analyzing big data helps eLearning professionals to offer a personalized experience to learners. It makes it very simple to identify the areas that need to be improved in the eLearning system. This allows developers to make the eLearning modules and courses more user-friendly as well as effective in delivering the intended instructions and training. Below are some of the fundamental benefits of big data in eLearning.

Find the Right Mode of Learning
Big data analysis can be very beneficial in identifying the specific courses or segments that are popular among the learners. For instance, you may find that most learners prefer visual charts and scenario-based activities than text-based exercises. When you have this data, you can create similar modules to make it more user-friendly. This not just allows learners to complete the courses quickly but also ensures that they remember the training and instructions for a long time.

Find the Structural Issues
You should ensure that the eLearning course of your organization is properly structured and well designed to convey all the necessary information to the learners. With the help of big data, you can easily pinpoint the assignments and exercises in the module where most learners fail. You may also find that most learners abandon a particular scenario-based task because of the difficulty in completing it. With this data, you can restructure the eLearning system and make it more organized.

Plan the Right Deployment Strategy
A careful assessment of the data collected by the eLearning system will help you to understand which particular deployment strategies are more profitable for your organization. Big data allows you to see whether your learners are using a Windows-based platform to access the online learning modules or if they prefer Android or iOS systems. This allows you to plan your deployment strategy wisely and figure out whether you need multiple OS delivery support or stick to a particular platform.

Revamp the eLearning Courses
Big data analysis also helps you to tweak the design of the eLearning modules aptly to suit the needs of users. For instance, if none of your learners has used the download or print option for a particular course in the past five years, then it indicates that learners can still get all the necessary training and instructions without that segment. This allows you to reconsider whether you should continue spending your time and money on updating that particular course or replace it with something more productive.

These are just a few benefits of big data in eLearning. Data collection is inevitable in eLearning and it is updated with each interaction. Therefore, you should use the best tools and LMS software to make the most of it. This way, you can design, deliver, and monitor eLearning content much more effectively.

5 ways in which machine learning can become a game-changer in the healthcare sector

Swathi Krishna — Fri, 22 Nov 2019 11:43:26 +0000

The healthcare sector is one of the biggest names in the list of investors for digital solutions over the past decade or so. But, a key thing to note here is that most of these investments have been on electronic medical records and making healthcare operations more sturdy and reliable. While these investments have borne the fruit, the impact it has on patients have quite turned out into a routine affair rather than something innovative. So what’s next for healthcare innovation? What technology platform will reinvent care delivery from traditional service models? The answer lies in machine learning and its larger umbrella – artificial intelligence. No, we are not talking about humanoids or artificial brains, but how digital systems being adopted across the healthcare spectrum can be tweaked with machine learning to drive more value for healthcare providers and patients.

Let’s explore 5 ways in which machine learning can empower the healthcare sector to transform service delivery:

Intelligent Robotic Surgery
Globally, the demand for robotic surgery is on the rise primarily due to the precision it offers for incisions and hence ultimately lower hospital stays for patients after surgery for healing. It has already proven to reduce hospital stay time by 21% according to studies. If you are to introduce machine learning in the picture, then a whole new dimension of possibilities are opened up for surgical procedures. By learning heaps of robotic surgical data from past procedures, intelligent algorithms can enable more accurate incision patterns and surgical techniques that require minimal cuts and stitches. Machine learning-enabled tools can also empower doctors to have greater control of surgical procedures in handling complex cases.

Clinical Analysis
From running advanced analysis on blood sample observations to decoding patterns within tumorous cells, machine learning can be the best friend of doctors to gauge precisely the medical condition of a patient. Powerful AI-enabled tools can simultaneously compare specimen behavior with millions of recorded medical history archives swiftly and offer insights into matching patterns of illness diagnosis. It might take current laboratory research and observation specialist days to compare and diagnose conditions that AI-enabled digital tools can find in seconds. This is a perfect example of how man and machine (machine learning) can collaborate to deliver value for all. These are the stories that need to be portrayed in contrast to the regular Hollywood themes of machines taking over mankind abruptly. The system has been recognized as such a worthy solution for medical diagnostics, that governments across the world are launching programs that involve regional health agencies incorporating AI-enabled research to boost clinical diagnostics performance and ultimately predict health status of citizens.

Training and Information Discovery
No matter how much ever technology evolves, a human doctor can never be replaced by machines or software bots. But on the contrary, this software can help make a doctor’s life much easier. Experience in medical procedures is a major criterion of how doctors build their skills and they constantly need to become knowledgeable of the latest happenings in their field of specialization in order to treat patients continuously. This is where machine learning can help doctors identify relevant research and knowledge materials from millions of medical records that may be available in different parts of the world. For a normal person, having to find relevant information from books and records that are so huge in number would be a Herculean task, but for a software bot, it takes only a couple of minutes maximum. This capability can help significantly in training and learning sessions organized for doctors, wherein detailed insights about medical practices being followed across the globe can be taught much better with relevant examples.

Medical Imaging
We have progressed significantly in medical imaging since the days of X-rays and today there are different imaging techniques that provide sharp and crystal-clear details on how various body functions occur over different conditions. But the ability to detect suspicious behavior of tissues, cells, etc. is often a painstaking job performed manually by doctors and imaging professionals. Machine learning can make this process easier and more accurate. Modern digital tools equipped with machine learning can easily identify anomalies from imaging records such as an advanced MRI of the brain or kidney or the spinal cord. It can identify potential patterns of deadly conditions such as cancerous growth in the early stages itself. It is always possible for the human eye to lose sight of microscopic details of a medical image, but trained AI algorithms will not miss these patterns. This can prove to be a critical application of machine learning as it will help identify potentially fatal diseases in earlier stages and help patients acquire necessary treatment at the earliest to avoid any consequences.

Smart Assistants
AI can be the best nurse for patients in an advisory role. Today there are thousands of smart wearable devices that people can purchase and wear on them. These devices can measure all vital body parameters from as simple as pulse rate to even blood sample evaluations. By tracking patterns in your body during activities such as breathing rate, pulse rate, and others, machine learning-enabled assistants can identify your optimal health condition. It can advise you to have a check on activities if anomalies are detected from usual patterns. For example, if the level of fatigue or tiredness increases exponentially while walking or jogging for a shorter distance than historically observed data, it could be an indication of declining health or stamina. Another aspect for intelligent assistants is when patients can chat with an AI assistant on their phones to know about potential ailments without paying a visit to the nearest clinic. They can upload results of blood tests and their physical activities that are tracked by wearables and the AI assistant would then run a powerful analysis of these results. By comparing them with millions of reference cases available across medical databases, the assistant can provide insights into how the person’s health parameters are doing now.

As you can see, the possibilities of machine learning and Artificial Intelligence in the healthcare sector are numerous. From becoming virtual nurses to patients to enabling doctors to diagnose fatal medical conditions faster, there is a wide scope of applications that AI and machine learning can find in this sector. While concerns about data security and patient privacy are always a question when it comes to research activities, machine learning can guarantee a safe and secure environment for analysis of patient data since it is fully automated and not controlled by human intervention. In the coming years, more consumer-facing healthcare initiatives will witness the proliferation of machine learning and AI and the results will be beneficial for patients as well as the industry as a whole.

Author Bio:

Tony believes in building technology around processes, rather than building processes around technology. He specializes in custom software development in USA, especially in analyzing processes, refining it and then building technology around it. He works with clients on a daily basis to understand and analyze their operational structure, discover (and not invent) key improvement areas and come up with technology solutions to deliver an efficient process.