An algorithm detects Parkinson’s in our voice three years before our first tremor. It processes Mel-Frequency Cepstral Coefficients while we order coffee, analyzing vocal patterns no human ear could distinguish. We sound perfectly healthy to everyone except the pattern recognition system.
Our retina contains the equivalent of 58 million pixels of data. The AI scans this information and outputs a prediction of macular degeneration six months before our vision begins to fail. We see normally while the algorithm processes our approaching blindness.
Our body operates through processes we have never accessed. Neurons fire across synaptic gaps using neurotransmitters we cannot sense. Our immune system identifies threats through protein configurations we cannot perceive. We are already alien to ourselves, processing information below the threshold of consciousness.
Between the input of our voice and the output of diagnosis lies a computational process that cannot be interrogated. Neural networks generate pathological classifications through operations they cannot explain. The system achieves 96% accuracy in detecting our skin cancer by processing light scattering patterns, but it cannot specify why this particular configuration of photons correlates with malignancy.
Our smartwatch transmits our pulse to satellites, predicting atrial fibrillation we haven’t experienced yet. Every heartbeat becomes data, every breath input to algorithms processing organ function we haven’t monitored consciously. The boundary between our body and the diagnostic apparatus dissolves. We are simultaneously patient and dataset.
To receive illness predictions before symptoms arrive creates temporal displacement, but we experience this differently. For some of us, the three-year Parkinson’s warning becomes a gift—time to prepare. For others, the prediction becomes a prison. We wake searching our voice for tremors, our movements for hesitation. Some ignore the predictions entirely, choosing present vitality over statistical shadows. Others obsess over percentages, transforming 94.7% accuracy into either false certainty or false hope. The privileged among us purchase preventive interventions; others carry knowledge without recourse. The system transforms us not into a single narrative of decline but into a multiplicity of relationships with our own projected futures.
Explainable AI attempts to illuminate the computational process but offers only approximations. When the algorithm flags potential melanoma, it might highlight the irregular border—features we already knew to examine. But it cannot articulate why this specific mathematical transformation of pixels triggered 89.3% malignancy probability rather than 12.7%. We receive heat maps showing which regions influenced its decision, yet these visualizations are themselves simplifications, translated from multidimensional mathematical spaces we cannot conceptualize. The “explanation” becomes another layer of mediation, a narrative overlay on mathematical processes that operate in dimensions we were never evolved to navigate.
The stethoscope required physical proximity, shared space between doctor and patient. The diagnostic algorithm processes our bodily data without physical contact, performing computational analysis of patterns we cannot detect.
Medical technology has often mediated self-knowledge. The thermometer quantified fever. The X-ray revealed internal structures. Now the mediation has become continuous. Our body generates data streams that flow between biological processes and silicon processing without interruption.
We are becoming bodies in a technological infrastructure that process themselves through systems that cannot process what processing means. In this computational asymmetry, we discover what it means to be diagnosed by our own data, reflected back through mathematical processes we cannot verify.
Blood flows through 60,000 miles of vessels we have never seen. Bacteria in our gut produce metabolites that influence our neural activity without our awareness. The algorithm adds a layer of computational opacity to biological opacity already present. It translates between biological complexity we cannot access and statistical correlations we cannot verify.
Perhaps this is where we learn to live: in the acknowledgment that we have never been transparent to ourselves. The algorithm does not make us more mysterious than we already were. It reveals the mystery that was always present.
Perhaps the work at hand is not to achieve understanding but to function within incomprehension. To learn how to make okay decisions based on partial information. To trust processes we cannot verify while maintaining skepticism about their outputs.
Perhaps we can learn to live with probability rather than certainty, with correlations rather than explanations, with statistical projections rather than definitive answers. This does not not have to be a resignation but it could be a more realistic relationship with the complexity that increasingly surrounds and constitutes us, and perhaps always was there. We were already full of processes inside and out that we do not fully understand. Now we are simply living inside more of them.
#symptom #stuffiwonderabout
