Decision-making with data: How multimodal AI sees the whole picture like a doctor

Doctors need additional information beyond a single symptom in order to make a diagnosis. If you call your doctor because your foot hurts, the information you provide will help them determine the root cause of the issue. They will then examine your foot, ask if you’ve had this type of pain before and maybe take an X-ray. 

Before they can assess what’s wrong and decide the best course of treatment, a physician first needs to gather a variety of information to understand the full picture. Much like a doctor, multimodal AI examines various types of data and information–not just one source–to create a more comprehensive result. 

Multimodal AI vs traditional AI models 

There are a few key differences in the way traditional AI models and multimodal AI models use and understand data. Significantly, traditional AI models were designed to process only one type of input, generally text data. This places certain constraints on what these basic models can do. Because it can only process and understand text, traditional AI can be narrow and limited in context, leaving gaps in results.

However, multimodal AI’s capabilities go beyond where traditional AI models leave off. Robust multimodal AI models are capable of analyzing and processing multiple modalities of data rather than just a single type, enabling complex reasoning and more advanced experiences and results.

The multimodal AI approach can be compared to the way a doctor considers a patient with symptoms or a condition that needs to be diagnosed and then treated. Before treating a patient, a physician takes into account a variety of data types that can be read, observed, felt and heard. This data is often sourced from things like physical examination, diagnostic tests, observing symptoms in real time, diagnostic imaging, family medical history and written or verbal description of symptoms. Once the doctor considers and analyzes the data, they can more confidently make an informed diagnosis to achieve the best outcome for each patient. 

Similarly, multimodal AI processes data from many diverse sources– audio, images, text, sensory data and video–simultaneously, which allows for a comprehensive and richer understanding. This leads to smarter and more accurate outputs because multimodal AI takes every input and distills the most relevant information needed to make decisions. Powerful multimodal AI models are faster and far more efficient than manually sorting and sifting through volumes of data. 

Cordoniq integrates data from various sources such as video, audio, text, images and documents into your existing multimodal AI model, delivering instantaneous business intelligence. Just like a doctor, multimodal AI combines diverse data to allow you to see the whole picture and make smarter, more accurate business decisions.

Richer and more precise results 

Because multimodal AI can simultaneously process and understand various data types, its results are more precise and nuanced than traditional AI models that are limited to text input.

These capabilities are what set multimodal AI models apart and give them the flexibility and versatility for real-world applications, including the healthcare sector. Some of the key advantages of multimodal AI models involve the way they analyze and use the data that is given and collected. For instance, multimodal AI enables more natural and intuitive interactions by processing multiple input types, such as voice commands and gestures. And by integrating multiple data types, multimodal AI systems can cross-validate information, reduce errors and improve workflows, allowing teams to make accurate decisions with confidence.

Other advantages of multimodal AI: 

• Provides better contextual comprehension because of the way it analyzes multiple types of data together
• Enables deeper interpretation of the data it processes and analyzes 
• Generates increasingly personalized results and better predictions of outcomes 

Real-world applications

Multiple industries, including healthcare, are incorporating multimodal AI into their processes and workflows for a variety of use cases. Here’s a look at some current real-world applications by industry and service: 

Healthcare

• Multimodal AI can analyze text, images, diagnostic tests and patient history to assist physician teams with complex diagnoses
• Reduces errors by cross-referencing data (e.g., combining X-rays and patient records for diagnoses)
• Helps pinpoint more precise image diagnostics

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Learning and training

• Assists in enhanced and customized user experiences in areas including learning languages, training and education, specifically because of the way it can process multiple data types
• Provides more accurate and personalized learning experiences, often in real-time 

Customer service 

• Multimodal AI-powered chatbots enable expanded customer service
• Processes voice commands, text and images simultaneously, making chatbots more interactive and effective in various customer service experiences 

Security and fraud detection

• Multimodal AI models allow systems to analyze video, audio, financial records, video footage and audio recordings
• Detects different types of fraudulent activity quickly and often more accurately than manual processes 

To make an accurate and informed diagnosis, doctors need to gather, observe and analyze relevant and complete information about their patient’s condition to develop a personalized treatment plan. Multimodal AI takes a similar approach to achieving an informed result. By integrating diverse types of data, conducting comprehensive analyses and processing data in real time, multimodal AI systems can make more accurate decisions, leading to better, personalized results and outcomes.