Diagnostic AI: Generative AI Solution for Radiologists

Diagnostic AI:A Gen AI-Powered Solution for Radiologists

Diagnostic AI is an impactful and prominent solution developed by Build Future AI using generative AI, advanced algorithms, and machine learning to assist the radiologist, making all operational tasks more efficient, faster, and seamless

About the client

The US-based client approached us to develop AI-enabled radiologist assistance for their imaging technology that helps reduce 20% of their time on CT scans and 15% of their time on radiographs. They wanted to employ the potential features of generative AI, like AI reporting, omni-impressions, dynamic worklists, and many more, elevating the efficiency and efficacy of the procedure. This AI integration aimed to automate several functions while maintaining accuracy at its peak. The enforcement of Deep learning automates feature representation without experts needing prior definition.

30%

of Radiologists employ Diagnostic AI in their clinical practices

20%

of Radiologists expect it to be accessed in the near future.

Problem STatement

A few months back, a US-based client and well-recognized radiologists contacted us about making their solutions more efficient and faster. They wanted to build a leadership-generative AI that eased prolonged working hours, elevating efficiency and accuracy in the field. Making Radiology more accurate, Diagnostic AI is expected to save 60+ minutes daily from its routine work. With Diagnostic AI, it’s easy to customize and practice seamless operations.

our solution

1. Diagnostic AI reporting

With our diagnostic AI development, we employ generative algorithms and advanced machine learning to automate the process of generating reports based on radiological images. The AI analyzes images and presents reports outlining findings, abnormalities, and recommendations for further choices.

Dictates up to 35+ fewer words for more precise and accurate reporting.

Dictates up to 2 times faster using current templates and processes.
Reduce the usage of the dictated words up to 90%.
Elevated focus on patient care and image quality.

2. Diagnostic AI Omni Impressions

Diagnostic AI shares comprehensive insights using AI algorithms, changing multiple imaging modalities and patient data. This feature allows radiologists to understand patients’ conditions, incorporates data from various sources, and delivers more accurate diagnoses and treatment plans.

Saves 60+ minutes per day analyzing quick reports impressions from dictated findings.

Dictates up to 2 times faster using current templates and processes.
Reduce the usage of the dictated words up to 90%.
Elevated focus on patient care and image quality.

3. Dynamic Worklist

Employing Dynamic worklist features utilizing AI algorithms prioritizes and organizes radiological studies based on urgency, complexity, and patient history. This feature enables practitioners to manage their workload critical cases or improve overall workflow efficiency.

Enhances 15% productivity by prioritizing performance, minimal workflow, and AI-driven assignment.

Automatic algorithm prioritization escalates goals and shifts responsibility.
Real-time updated ranking of new studies and radiologists at the right time.
Provide seamless and easy collaboration among the team members over workflow.

4. Automated Follow-Ups

Our development automates the process of scheduling and conducting follow-up examinations and managing previous imaging studies and clinical findings. It analyzes patient data and recommends appropriate follow-ups, ensuring timely monitoring of the patient’s condition and progress and the early detection of abnormalities.

97% Assured Follow-up care and recommendations over incidental findings and reports.

Shortlist and identify follow-up recommendations from radiology reports.
Automates communication, including reminders and process scheduling.
Tracks the response and manages follow-up recommendations and ROI.

Empowering Radiologists with Predictive Precision: Diagnostic AI Transforms Care Through Deep Learning

We employ machine-learning algorithms with the development of Diagnostic AI, encompassing predictive and deep learning models that mimic human decision-making working on Artificial Neural Networks.

The benefit of Diagnostic AI in Radiology

Improved Reporting

Early Detection

Improved Prioritization

Improved Accuracy

Optimized Radiology Dosing

Reduced Radiation Exposure

Enhanced Image Quality

Improved Satisfaction

Faster Diagnosis

Improved Patient Care

Challenges & solutions

Efficient Reporting Process

Developing a system (Diagnostic AI) that can generate the most accurate and detailed reports from radiological images was significantly challenging. To overcome this challenge, we implemented generative algorithms and advanced machine-learning techniques to automate the reporting process. It not only saves time but also maintains accuracy.

Comprehensive Insights Generation

Generating comprehensive insights using multiple modalities and patient data requires more sophisticated and advanced AI algorithms. We employ advanced AI algorithms to analyze diverse data sources, enabling radiologists to gain comprehensive insights. This feature allows for a holistic understanding of patients’ conditions and improves the patient’s diagnosis.

Workflow Optimization

Prioritizing and organizing the radiological studies that depict urgency, complexity, and patient history cause challenges in workflow optimization. We employ dynamic worklists using AI algorithms, prioritizing studies and optimizing workflow efficiency. This advanced feature allows radiologists to work on critical cases and improve productivity.

Automated Follow-ups

Automating the whole process of scheduling and conducting follow-ups using radiographic imaging needs seamless integration with the existing systems. We integrate Diagnostic AI with the existing systems, automating the follow-up, recommendations, and scheduling process. It ensures timely monitoring of the patient’s condition, early detection of abnormalities, and patient care.

Communication & Collaboration

Establishing effective communication and collaboration among team members was challenging. However, in Diagnostic AI, we employed seamless communication and smooth collaboration features, allowing team members to collaborate effectively over workflows. This feature enables real-time updates and notifications, ensuring effective coordination.

Efficiency & Time Savings
Diagnostic AI noticeably enhances the overall efficiency of radiologists by automating several tasks, such as reporting, workflow prioritization, and scheduling follow-ups. It reduces the time spent on procedures and improves decision-making and patient care, elevating productivity and reducing burnout.

Accuracy and precision
Employing generative AI algorithms and machine learning brings more accuracy and precise reporting and diagnosis. Diagnostic AI adds automation to the reporting process that accelerates report generation and minimizes the risk of human errors, resulting in more proficient outcomes.

Comprehensive Insights and Diagnosis
Diagnostic AI analyzes diverse and vast data sources and maximizes the radiologist’s capabilities in generating comprehensive insights. It integrates diverse modalities of holistic understanding, facilitating informed diagnosis and treatment planning.

Workflow Optimization
Implementing a dynamic worklist based on an AI algorithm improves workflow efficiency and helps with task management based on urgency and complexity. It streamlines the work more effectively and enhances overall productivity.

Automated follow-ups and patient care
Having automated follow-ups elevates scheduling and timely recommendations. It has clear insight into the convenient monitoring of the patient’s condition and the early detection of abnormalities. It has a proactive approach, improving patient outcomes, enhancing efficiency, regulating follow-ups, and reducing the risk of delays.

Enhanced communication and collaboration
To maintain a seamless work balance, management, effective communication, and collaboration play a prominent role in radiology. Diagnostic AI facilitates real-time updates, seamless collaboration, better coordination, and task management.

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frequently asked questions

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Is AI as a service the same as SaaS?

No, they are not the same. AI as a service (AIaaS) is a model that employs cloud-based technologies using third-party APIs, providing services to customers. AIaaS provides cost-effective services, and off-the-shelf tools implement AI techniques, providing flexibility, usability, and scalability.
Software as a Service (SaaS) is a cloud-based service model that ensures the client can get on-demand access to provide ready-to-use application software. SaaS developers manage products such as applications, data, servers, and other user-friendly features.

What are the benefits of AI as a service?

AIaaS allows businesses to employ the prominence of AI’s machine learning, deep learning, and advanced API algorithms, making it whole at significantly lower cost. Using AI in business can improve their core functionality, eliminating the need for training and other decision-making areas. AI provides flexibility, ease of use, and scalability to businesses, resulting in cost reduction, speed, simplicity, customization, and efficiency in the overall process.

What is the future of the AIaaS?

AIaaS improves business automation with the seamless integration of chatbots and digital assistants that handle numerous queries, making the simplest conversations with customers and answering basic queries from employees. AI helps quick decision-making by analyzing vast amounts of data and converting findings into convenient visual formats. Focusing on the latest trend in AI, it is expected to offer continuous growth, cost-effective results, faster implementation, and lower entry barriers.