Category: AI blog

Introduction

As the interest in AI grows, so does the number of applications. One area that is seeing spectacular growth of late lies at the intersection of AI and visual analytics. The AI in computer vision market size was pegged at $29.77 billion in 2023, and total AI in computer vision revenues are projected to grow by 24.6% 2024 to 2030 to reach almost $138.84 billion (Stellar Market Research). So, let’s take a closer look at where CV is making an impact.

Computer vision is now frequently utilized in the healthcare system. Examination of visuals, radiographs, and photos is vital in any medical diagnosis. Progress in computational vision promises not only to increase the speed of diagnostics but also bypass mistaken diagnostics and reduce medical costs by analyzing ultrasound pictures, MRIs, and CT scans.

In the article, let’s see how vision AI is revolutionizing healthcare. Then let’s turn our gaze toward other sectors like connected factories, smart utilities, and smart buildings.

Vision AI in Healthcare

Vision AI involves the application of AI and computer vision technologies in the analysis and interpretation of medical images and other visual data in healthcare. Advanced algorithms and ML models in this technology process and interpret the information contained within independent files of several types of medical imagery, such as:

• X-rays
• MRI scans
• CT scans
• Ultrasound images
• Pathology slides
• Dermatological images
• Endoscopy videos

Use Cases

1.      Medical Image Analysis

One prominent example is the collaborative study by Warwick, King’s College London, and several NHS sites. Their AI model was trained on 2.8 million historical chest X-rays from more than 1.5 million patients to scan for 37 possible conditions. It achieved an accuracy rate of 94%, thus performing at least as well as human radiologists who read chest X-rays for 35 of the 37 conditions present at the time a patient’s X-ray was taken. This application means quicker diagnoses and quite possibly lifesaving interventions. (King’s College London)

2.      Wound Care Management

Vision AI can identify wound images for wound healing, infection tracking, and recommending treatment plans. All of this enhances the outcome for patients who have chronic wounds, such as those resulting from diabetes ulcers, by shortening healing time and reducing associated costs with long healing times.

3.      Remote Patient Monitoring

Vision AI can be game-changing in remote patient monitoring. Analysis of images and videos captured from patients’ homes can provide healthcare providers with the ability to track vital parameters, monitor chronic conditions, and identify potential complications at their incipience. This improves access to better care for the patient and permits earlier intervention in health conditions.

4.      Tumor and Cancer Detection

Vision AI has huge potential, especially in the early detection of cancer. One study regarding breast cancer applied computer vision and deep learning to construct a new framework that automatically detected it. The framework was trained with an ultrasonic image dataset and gave a very high accuracy of 97.18% in breast cancer detection under rigorous cross-validation test criteria. Strides such as these are leading to earlier diagnoses with much more effective treatment strategies.

5.      Healthcare Research and Medical Trials

It can process medical images and data much faster than human researchers. This could quicken drug discovery, the analysis of clinical trials, and the development of personalized treatments. In so doing, vision AI automates tasks and discovers otherwise hidden patterns within medical data at superhuman rates, thereby advancing quality and outcomes in healthcare research worldwide.

Benefits

1.      Cost Reduction

A report by McKinsey and Harvard researchers estimated that AI could save the U.S. healthcare system as much as $360 billion annually. That is because AI’s potential for cutting down on processes, minimizing errors, and bringing efficiencies into resource allocations across a wide swathe of healthcare functions holds huge cost-saving potential.

2.      Personalized Treatment Plans

Vision AI interprets massive quantities of patient-related data, especially medical images, in such a way that even the subtlest recurring patterns can yield personalized intervention in treatment. This results in more targeted care that translates to better patient outcomes.

3.      Automated Quality Control

Vision AI is used in automated quality control for the analysis of medical images to catch inconsistencies or mistakes in the scans. This ensures that the diagnoses remain accurate and reduces the risks of missing any condition.

4.      Enhanced Efficiency

It is observed in a study that about 64% of patients are comfortable with AI offering support around the clock. AI virtual nurse assistants can do routine tasks such as answering medication questions, forwarding reports, and scheduling appointments. This helps free up clinical staff for direct patient care, where human judgment and interaction are most important.

5.      Fraud Detection

Healthcare fraud costs the US approximately $68 billion annually, according to the NHCAA. AI has the potential to identify such strange patterns in insurance claims, indicating things like billing for services never performed or running unnecessary tests. It is likely that, by tracing and thus preventing such fraudulent activities, AI would reduce health costs and eventually lower insurance premiums to consumers.

Vision AI Beyond Healthcare

Connected Factories

Connected factories, otherwise known as smart factories or “Industry 4.0,” are changing the notion of manufacturing with the implementation of vision AI. Advanced manufacturing environments make use of AI-powered computer vision systems in deriving better productivity, quality control, and safety.

Key applications of vision AI in connected factories include:

• AI-powered cameras for high-speed product inspection and real-time defect detection.
• Predictive maintenance using visual and thermal data to prevent equipment failures.
• Production line optimization by data-driven analysis on bottlenecks.
• Real-time workplace safety monitoring and hazard detection.
• Automation of inventory tracking and reordering to a higher degree of accuracy at reduced costs.
• Next-generation robot systems that learn to adapt for task completion.
• Accurate guiding of automated assembly processes to reduce errors.

Smart Utilities

Smart utilities involve modernizing the traditional utility services of electricity, water, gas, and waste management through AI, the Internet of Things (IoT), and data analytics to bring efficiency, reliability, and sustainability into the utility services being offered with increased customer experience and resource management.

Smart utilities would typically constitute:

• Advanced metering infrastructure.
• Real-time monitoring and control systems.
• Predictive maintenance capabilities with automated fault detection and recovery.
• Data-driven decision-making and execution processes.
• Improved platforms for customer engagement.

Smart Buildings

Smart buildings are designed structures that incorporate automated processes and integrated technologies into various operations’ control and management within a building. The integration of Internet of Things (IoT) devices, sensors, and AI creates an environment that is more efficient, more comfortable, and more sustainable.

Key features of smart buildings include:

• Occupancy detection and space utilization optimization in offices and public spaces.
• Automated HVAC and lighting control based on real-time occupancy and activity levels.
• Improving security through AI-powered surveillance and anomaly detection.
• Facial recognition and gesture-based interfaces for touchless access control.
• Automated parking management and vehicle identification in garages.
• Predictive maintenance of the building systems from visual and thermal imaging data.
• AI-assisted enhanced emergency response through evacuation route planning and crowd management.

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Introduction

Deep learning technologies like AI and machine learning are transforming business applications to become more intelligent in 2024 and beyond. This development has wide-reaching implications from healthcare to the education industry.

Customers are already integrating AI/ML into their software applications. However, they continue to face challenges like:
● Choosing the best AI-powered data model for various use cases.
● Lack of internal knowledge and domain expertise in foundational models.
● Operationalizing AI models for quality and debugging purposes.

With Databricks, organizations can overcome these challenges readily. They can exploit the full potential of the data at hand and also extend their AI initiatives into the future by building Generative AI applications for a variety of use cases. Using its Foundational Model APIs, Databricks provides immediate access to Large Language Models (LLMs) like Llama 2 and MPT. Similarly, with External Models, organizations can add endpoints for accessing AI models, and external (or outside) Databricks. For instance, Azure OpenAI GPT, Anthropic Claude, and AWS Bedrock.

Possible use cases of Databricks in the AI and machine learning domain

In this blog, let’s discuss 6 possible use cases of Databricks in the AI and machine learning domain:

1. Predictive Analytics
Using AI-powered predictive analytics, organizations can analyze data patterns for business risks and opportunities. Predictive analytics is not possible without Big Data. For instance, in the engineering domain, predictive analytics works on data retrieved from machine sensors, instruments, and connected systems.

With Databricks, organizations now have a scalable collaborative platform for data analysts to build and deploy AI-powered predictive models. For example, retailers can leverage Databricks to analyze customer data and predict purchasing behavior and market trends.

Additionally, with machine learning algorithms, retailers can optimize their inventories and personalize their marketing campaigns for a particular region or demographic.

2. Energy production and distribution
The global energy & utility industry is faced with multiple challenges like low energy production, rising costs, and inefficient distribution. As the industry gradually shifts to renewable energy, companies need to improve the efficiency of their energy production and distribution.

With the Databricks platform, energy companies can leverage real-time data from various sources including weather forecasts and IoT-connected sensors. Through real-time data analytics, Databricks can improve their decision-making process, thus delivering a more reliable and sustainable energy system.

Similarly, machine learning models in the energy sector can deliver predictive maintenance based on the data collected from connected machines. This helps in reducing operational failure and machine downtime.

Here’s a customer success story of how Shell leveraged Databricks to modernize its global operations.

3. Personalized healthcare
Healthcare companies can now deliver patient-centric care by combining the power of data with AI technology. By unifying data analytics and machine learning, healthcare organizations can improve patient engagement and precision care.

With Databricks, healthcare providers can easily analyze large volumes of biomedical and genomics data. Healthcare researchers can identify genetic variations or monitor the progress of any disease. This helps them develop personalized care for individual patients. Similarly, Databricks can innovate precision medicine, resulting in accurate diagnoses and improved patient outcomes.

AI-powered precision prevention is leveraging population data to identify the patients who are at the highest risk of any disease or infection. Here’s how Databricks enabled Walgreens to personalize their patient experience.

4. Supply chain management
In a complex global market, supply chain management is critical for many organizations. Some of the key elements of supply chain management include supply chain planning, sourcing, forecasting, production, and inventory management.

With the Databricks platform, supply chain companies can build a resilient and predictive supply chain. Along with supply chain planning, Databricks can scale and fine-tune supply chain forecasts to predict market demand. Real-time analytics can help companies monitor and respond quickly to supply chain disruptions or global events.

Similarly, Databricks’ machine learning capabilities enable organizations to build efficient predictive models that can optimize the entire supply chain. Apart from improving demand forecasting, organizations can leverage machine learning to reduce their inventory costs and optimize their logistics operations.

Here’s a customer success story of how Databricks transformed the digital ecosystem of an Australian-based rail transportation company.

5. Smart buildings & utilities
Real-time data is the driving force for smart buildings and public utilities. The key to building smart buildings and utilities is to harness the generated data to make informed decisions about energy efficiency and floor layouts. While smart buildings collect a lot of data, they are difficult to understand, particularly if they are integrated with other technology systems.

The AI-powered Databricks platform can assess real-time data from existing facilities and identify potential bottlenecks or issues. For instance, power consumption in an underutilized conference room or an unoptimized utility grid performance.

Here’s a customer success story of how Databricks enabled data-driven insights for a power generation company.

6. Customer churn
Companies with the highest customer retention grow their revenues 250% faster than their competitors. Hence, more companies are focusing on retaining customers for long-term growth. Customer churn can occur at any stage of the customer lifecycle. Most organizations are unable to accurately predict when individual customers are likely to churn or leave.

According to Microsoft, Databricks can predict customer churn with 90% accuracy. Additionally, Databricks offers a feasible recommendation strategy to prevent customer churn. Besides, Databricks provides modern techniques like neural networks and gradient trees to detect customer churn. Through proper configuration and evaluation, these techniques can detect subtle shifts in customer data patterns, thus providing more accurate models.

Conclusion

With the mainstreaming of AI applications, organizations are adopting a data-driven approach to their decision-making process. With its unified analytics, Databricks is the best platform to deliver efficient analytics. With this cloud-based platform, more companies can develop and deploy AI-powered applications across use cases.

As a Consulting and SI partner for Databricks, Pratiti Technologies is empowering clients across industry domains with our advanced Data and AI capabilities. With this robust data platform, our customers are efficiently managing their data processes with real-time insights.

Do you want to know more about our offerings? Contact us today!

Our Services

Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform