AI Has New Capability To Sniffs Out Disease

An electronic nose (e-nose) is a sensing device that mimics the human sense of smell to detect, identify, and quantify odors or flavors. Res...

An electronic nose (e-nose) is a sensing device that mimics the human sense of smell to detect, identify, and quantify odors or flavors.

Researchers at MIT have developed an AI-powered "electronic noses" that being trained to detect diseases by analyzing the invisible chemical signals emitted from our bodies, potentially identifying illness before a person even feel sick. This new invention employ the electronic nose to detect human breath, and whiff of body odor, or a skin sample and instantly reveal the presence of disease — all without needles, scans, or symptoms. Thanks to a new class of scent-based artificial intelligence, that made this feat becoming a reality.

An electronic nose (e-nose) is a sensing device that mimics the human sense of smell to detect, identify, and quantify odors or flavors.

This radical advancement blends volatile organic compound (VOC) analysis with machine learning to create diagnostic systems that operate faster, more affordably, and less invasively than current methods. These electronic noses could help revolutionize global healthcare, especially in early cancer detection, infectious disease control, and neurological monitoring.

The Science of Scent

Every disease leaves a molecular trail. Metabolic changes caused by conditions like lung cancer, Parkinson’s, and liver disease subtly alter the cocktail of VOCs released by our bodies. Humans can’t detect these faint odors — but animals can. Trained dogs, for example, have been able to sniff out COVID-19 or cancer with over 90% accuracy. The problem? Dogs tire easily, require costly training, and aren't practical for mass screening.

Applications of Electronic Nose and Computer Vision.

Researchers are now recreating the canine sense of smell in hardware. AI models are being paired with sensor arrays that mimic olfactory receptors. These "e-noses" can identify unique VOC fingerprints associated with various diseases. For example, scientists at MIT trained a deep learning model to analyze sensor data and distinguish between different types of cancer — with accuracy comparable to trained medical professionals.

Applications

One promising application is in rapid breath-based diagnostics. In 2023, researchers at the University of Washington developed a prototype that could detect early signs of tuberculosis from a breath sample — a huge step forward for regions where access to labs is limited.

Another notable case is the use of AI scent detection in Parkinson’s disease. British researchers are exploring how a “musky” skin odor found in patients can be used as a reliable early biomarker. AI systems trained on such signals could offer earlier and more reliable detection long before traditional symptoms like tremors appear.

Beyond diagnosis, AI scent analysis could also be used to track disease progression, monitor treatment effectiveness, and even personalize medication regimens. For instance, fluctuations in breath VOCs could indicate how well a chemotherapy regimen is working — allowing doctors to adjust treatment in near real-time.

The implications for pandemic response are also massive. In crowded settings like airports or schools, AI scent sensors could serve as fast, passive disease detectors — identifying asymptomatic carriers before outbreaks spread.

Challenges

Despite its promise, AI olfaction faces several hurdles. VOCs can be influenced by diet, environment, and stress, which makes separating disease signals from background noise difficult. There’s also the challenge of dataset bias — training data that doesn’t represent diverse populations could lead to inaccurate results.

Then there are privacy concerns. If breath or body odor becomes a medical signature, how do we protect that data? Could insurers or employers misuse it? Just like facial recognition and biometrics, scent-based diagnostics will require strict ethical guidelines and data protections.

The path forward will likely involve integrating scent analysis with other AI diagnostics — combining olfaction with computer vision, blood tests, and wearable data to create a more holistic and precise medical profile. Already, startups like NanoOS and Zebra Medical are investing in multi-modal AI platforms.

If the current pace continues, breath tests may become as routine as taking your temperature. Instead of waiting for symptoms to send us to the doctor, machines might tell us we’re sick before we ever feel it — quietly sniffing their way to better health outcomes.

AI-driven scent detection is more than a technological novelty — it's a paradigm shift in how we think about health. By identifying the invisible signals our bodies emit, machines may soon catch disease in its earliest, most treatable stages. The next major breakthrough in healthcare might not come from a lab test or MRI, but from something we’ve always overlooked: the scent of sickness.