AI-based systems for disease detection from audio signals are being developed to be used as a supportive resource in the healthcare system, but ethical issues surrounding them must first be adequately addressed.
AI has grown significantly in recent years, and AI-based disease detection systems from audio signals are being developed alongside expert-designed feature extractors. Adversarial nets and traditional statistical methods can be used to analyze data which is then processed using deep learning algorithms. Deep Learning models make use of artificial neural networks which can detect potential warning signs or signals related to a certain disease or condition. AI algorithms have the potential to be used in medical research as a supportive resource for healthcare professionals, but ethical issues regarding privacy protection must first be adequately addressed.
AI-Based Speech Analysis
Artificial intelligence has grown significantly in recent years, impacting various sectors such as the healthcare industry. AI-based systems are increasing in popularity for their accuracy and robustness, but must prioritize explainability when dealing with health information.
Health-oriented AI research focuses on human-centered models that improve overall efficacy of care point to future applications for emerging AI technologies.
AI Advancements in Speech
Recent advancements in the audio domain with a specific focus on speech data have led to the development of automated, AI-based technologies for disease detection.
In 2022, the numerous incredible advancements made by Google’s speech AI and natural language technologies teams were made available to customers for use cases ranging from robots that help foster healthy childhood development to customer service improvements based on phone calls and voicemails.
Capturing noteworthy advances in the field, Google announced a visual user interface for its Speech-to-Text API (which supports over 70 languages) as well as enhancements to its Text-to-Speech API with custom voices and content classification based on large language models. The company also rolled out Neural2 Voices for the TTS API, which eliminates frustration in voice services without a network connection via Speech On Device support.
Deep learning methods are increasingly used alongside expert-designed feature extractors and classical machine learning methodologies. Limitations for speech-based disease detection systems include data availability such as patients and healthy controls from different ages, languages, and cultures.
AI-Based Disease Detection from Audio: Exploring Features, Signals and Algorithms
AI is a term used to describe technologies that are able to solve complex tasks, including pattern recognition or creative tasks. AI research has advanced rapidly over the last few decades, mainly due to breakthroughs in machine learning (ML). ML utilizes parametric algorithms and data training methods which allow AI systems to make decisions based on given parameters.
The most successful form of ML currently is deep learning (DL), which uses artificial neural networks (ANNs) with hierarchical structures of neurons that analyze processed data points by matrix computations and non-linear functions. Traditional statistical methods may also be utilized but they are not considered “true” AI.
Adversarial Nets is a generative model estimation procedure that leverages the existing backpropagation and dropout algorithms while sidestepping some of the difficulties presented by previous deep learning models. The generative model is pitted against an adversary: a discriminative model that learns to determine whether or not sample data is from the correct distribution, designed to be analogous to counterfeiting – with the former trying to create counterfeit currency and use it undetected, and the latter being akin to police looking for such fakes. Competition between these two teams drives both teams improve their respective methods until counterfeits are indistinguishable from genuine samples; this process results in specific training algorithms applicable for many kinds of models through optimization.
AI algorithms are increasingly being used in medical research, however research based on audio data is still limited. Audio signals usually come from human speech production and can encode information that can be useful for AI systems. Features derived from audio signal representations, such as the time or the frequency domain, can be used to detect diseases like COVID-19 and are a good basis for AI systems to do so.
Speech modality is a promising area of AI research, particularly in the medical domain.
Audio data can be processed and analyzed to help diagnose infection and diseases such as COVID-19. AI algorithms use signals encoded with relevant information to process task-related data. Examples of these signals can include visual or auditory information, such as those derived from human speech production. Due to environmental and internal factors including diseases, audio approaches are needed for medical research in order to detect any changes in speech quality or content.
The raw form of audio signals are time- and value-quantized one dimensional signals that must first have features extracted before they may be suitable for further analysis using AI algorithms. Traditional feature selection requires an experienced hand to carefully select features with potential relevance to a specific task.
In some cases, the spectral audio signal representation can identify differences between healthy individuals and those suffering from respiratory disease like COVID-19, reflecting certain coarseness in the speech of sick patients manifested inherently from within their vocal apparatus due largely to neurological disorders or environmental influences in general that imparts changes onto their temporal air pressure waveforms entering into microphone detectors for computer processing.
Speech-based AI systems are a promising technology for the detection of diseases such as COVID-19. These systems could be used in clinics, by local general practitioners or specific test centers. They would collect speech material from the patient and then immediately analyze it with an AI system to identify potential warning signs or signals related to a particular disease. The results would then be interpreted by healthcare professionals and inform further diagnostic steps or intervention procedures with the patient.
Final words on this unique example of how organizations can use AI for greater good
AI has grown, and the advancements have opened new doors of opportunity for applying this amazing new technology. Here, we’ve talked about how we might apply artificial intelligence to empower medical professionals with tools that can detect disease only from audio signals using adversarial nets and traditional statistical methods, and what we could do by feeding this dataset to further processing using deep learning algorithms. Deep Learning models make use of artificial neural networks which can detect potential warning signs or signals related to a certain disease or condition.
AI algorithms have the potential to be used in medical research as a supportive resource for healthcare professionals, but only tomorrow will reveal the new ways we will use AI for Business, what this will mean for the future of business intelligence, and to how this will impact leaders to make better decisions for their organizations.