How is the medical field benefitting from AI- 2022?

May 3, 2022

In the field of medicine, both AI and robotics have a lot of abilities. AI and robots are increasingly a portion of our healthcare ecosystem, just as they are in our day-to-day lives.

What does AI in healthcare mean? Where are we lying in terms of executing AI in the current healthcare system and where will AI take the future of the existing medical industry?

Table of Content

  1. Overview
  2. How does AI complement Healthcare support?
  3. Use cases of AI in the medical industry 
  4. Future of AI in healthcare
  5. Conclusion

Overview

All the conventional medical approaches like Traditional analytics, or clinical decision-making mechanisms, have a variety of detriments when compared to AI. With a lot of data pumping in AI is on the optimal flank as learning algorithms interact with this data regularly, they can evolve more comprehensive and true, giving people unparalleled insights into care procedures, treatment variability, patient outcomes and diagnosis etc.

How does AI complement Healthcare support?

Because of its substantial role in a productive and healthy society, healthcare is one of the most integral areas. The use of AI in healthcare can mean the antagonism between life and death. Doctors, nurses, and other healthcare personnel can boost from artificial intelligence in their regular jobs. 

AI in healthcare has the potential to enhance patient health outcomes by enhancing preventative care and quality of life, as well as enabling more precise diagnosis and treatment. AI can assist in the prediction and tracking of the spread of dangerous diseases by assessing data from the government, healthcare, and other sources. It can be a vital tool in the global fight against diseases and pandemics.

Use cases of AI in the medical industry 

The following are six areas where AI's potential exceeds that of traditional therapeutic methods.

Keeping Well: AI assists individuals to stay healthy so they don't need to see a doctor as often, if at all. AI and the Internet of Medical Things (IoMT) are already being used in consumer health apps to motivate healthier behavior and assist with proactive management of a healthy lifestyle. It entitles patients to take charge of their health and well-being.

Decision Making: Predictive analytics can help in quick clinical decision-making and actions, as well as to improve treatment by aligning ample health data and with suitable and timely judgements it can prioritize size administrative activities.

Treatment: AI can assist doctors in taking a more holistic strategy for illness management, better-coordinating care plans, and assisting patients in managing and adhering to long-term treatment programs.

Early detection: AI is already being used to diagnose illnesses more specifically and in the initial stages. Such as in cancer, a large percentage of mammograms provide disingenuous findings, resulting in one out of every two healthy women being diagnosed with cancer. AI is allowing mammograms to be reviewed and translated 30 times quicker with 99 percent accuracy, curtailing the need for needless biopsies.

Diagnosis: It enables healthcare companies to use cognitive technology to unlock massive volumes of health data. There are technologies that can evaluate and retain considerably more medical data than any person, including every medical publication, symptom, and case study of therapy and reaction from around the world. 

End-of-life care: In elderly people, loneliness is a common occurrence and as they begin to age more, dying illnesses such as dementia, heart failure, and osteoporosis become common. AI has the potential to transform end-of-life care (care of the elderly) by allowing patients to be independent for extended periods, decreasing the need for hospitalization and care facilities. AI, paired with developments in humanoid design, is allowing robots to hold 'conversations' and other social interactions with people to keep aging minds strong.

Future of AI in healthcare- 

The most arduous impediment for AI in healthcare is convincing its acceptance in daily clinical practice. Clinicians may eventually gravitate towards activities that require specialized human abilities, such as those that require the highest degree of cognitive function. 

Perhaps in the future, the only healthcare practitioners who will miss out on AI's full potential are those who refuse to collaborate with it.

What would a future where AI is integrated with every sector of the healthcare sector look like? 

Below are some of the hypotheses that researchers and scholars believe would help the healthcare industry in gaining more trust and in being more reliable.

Mind and Machine unification through brain-computer interfaces: Because of neurological illness and injury to the nervous system, some patients' abilities to talk, move, and connect meaningfully with others can be taken away. Artificial intelligence-assisted brain-computer interfaces (BCIs) may help in restoring such essential experiences to individuals who worry they may be lost forever. We can decode the neurological activities linked with the intended motion of one's hand using a BCI and artificial intelligence. Patients with ALS, strokes, or locked-in syndrome, as well as the 500,000 individuals worldwide who suffer spinal cord injuries each year, might aid extensively from the integration of AI and BCIs.

AI-enabled Radiology Tools: MRI machines, CT scanners, and x-rays provide radiological pictures that provide non-invasive insight into the inner workings of the body. Many diagnostic procedures, however, still rely on actual tissue samples taken through biopsies, which come with dangers such as infection. Experts expect that artificial intelligence will allow the next generation of radiological instruments to be inclusive and factual enough to eliminate the requirement for tissue samples.

Expanding healthcare in formulating and poor regions: In developed countries across the globe, scarcity of qualified healthcare personnel, such as ultrasound technologists and radiologists, can severely limit access to life-saving care. By taking over some of the diagnostic duties that are traditionally handled by humans, artificial intelligence may be able to help mitigate the impacts of the considerable scarcity of competent clinical workers.

Artificial intelligence imaging techniques can scan chest x-rays for symptoms of TB with a degree of accuracy that is frequently equivalent to people. This capacity might be made available to physicians in low-resource locations via an app, eliminating the requirement for a diagnostic radiologist on site.

Advancing the use of Immunotherapy for cancer treatment: Immunotherapy is one of the most promising therapies for cancer treatment. Patients may be able to win against risky tumours by bombing them with their immune system. However, prevailing immunotherapy alternatives merely assist a microscopic percentage of patients, as oncologists still lack a valid and explicit approach for inferring which patients may profit from this treatment. 

AI and ML algorithms can synthesize very complex information and open up new avenues for tailoring medicines to a person's genetic composition.

Limiting the threat of antibiotic resistance: Antibiotic resistance is becoming an increasing hazard to people all over the world, as a result of the misuse of these life-saving drugs, superbugs exempt from antibiotics have emerged. Multidrug-resistant infections have the potential to wreak havoc in hospitals, killing hundreds of people every year.

Data from electronic health records can advance the detection of infection trends and the identification of people at risk before they develop symptoms. Using machine learning and AI to drive these analytics can augment their exactitude and give healthcare practitioners with faster, more accurate alerts. AI technologies can meet or exceed expectations in terms of infection management and antibiotic resistance.

Personal health monitoring devices: Almost every customer now has access to gadgets with sensors that may collect useful health information. A surging proportion of health-related data is created on the road, from cell phones with step trackers to wearables that can detect a heartbeat around the clock. Amassing and analyzing this information, via apps and other home monitoring devices, as well as complementing it with information supplied by patients, can provide a peculiar perspective on individual and population health. 

Artificial intelligence will be critical in extracting useful information from this huge data source.

Selfies as a Powerful diagnostic tool: Keeping with the theme of leveraging the potential of portable devices, experts feel that pictures acquired from cellphones and other consumer-grade sources will be a valuable addition to clinical quality imaging, particularly in underprivileged communities or developing countries. Every year, the quality of mobile phone cameras improves, and they can now provide photos that can be analyzed by artificial intelligence systems.

Conclusion-

Stakeholders, healthcare experts & inventors are all itched about the potential of AI in the healthcare industry. In the coming time, AI will direct a new age of clinical excellence and exciting advancements in inpatient care by powering a new generation of tools and systems that make doctors more aware of subtleties, more efficient when giving treatment, and more likely to get ahead of developing issues.

The options are left to the imagination of the reader. But one thing seems certain: AI will pave the way for a prosperous and healthy future.
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This is a decorative image for Project Management for AI-ML-DL Projects
June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.

 

The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.

 

In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.

 

Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:

Wiom

Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 

TechVantage

As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online

Manthan

As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 

NetraDyne

Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.

 

Helpshift

Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 

Facilio

An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.

 

In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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