Aether
Healthcare makes sense when data has memory!
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Aether Admin

Aether - Healthcare makes sense when data has memory!

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Aether's an AI-powered longitudinal health platform that combines a patient-owned health record with a lightweight EHR for doctors. It ingests fragmented medical data from labs, hospitals, clinics, and documents; organizes it into a longitudinal health graph for each individual. By learning from data over time, Aether surfaces trends, changes, and early risk signals missed in episodic care. Built for India’s fragmented healthcare system, Aether is already used by 25,000+ patients, 3 hospitals

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Aether Admin
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Hi Product Hunt, I’m Akash, founder of Aether. This is personal. A few years ago, my mom’s cancer went undiagnosed until it was too late. Her medical records were scattered across portals and PDFs. I found myself copying reports and lab results into Excel, manually entering values and plotting charts, hoping a pattern would emerge. It shouldn’t take a desperate family member with a spreadsheet to connect the dots. That experience is why I built Aether. Aether is a longitudinal health platform that combines a patient-owned health record with a lightweight EHR for doctors. Under the hood, we’re building a structured health graph and medical AI that can ingest any medical data, standardize it to interoperable formats like FHIR, and organize it into a living timeline that can be shared with doctors, caregivers, and family. The goal is simple: help people and clinicians see patterns across diseases, medications, labs, and imaging early enough to matter. Happy to answer questions and hear feedback.
Aether Admin
Maker

One idea that deeply shaped Aether comes from rare disease research.

A recent npj Digital Medicine paper shows that rare diseases are underdiagnosed not because data is missing, but because medical context does not compound over time. Diagnoses are provisional. Labels are noisy. Learning has to be longitudinal.

This resonated personally. My mother was diagnosed with an extremely rare cancer only days before her death. Her records existed, but no one ever had a complete picture.

I wrote a longer reflection on what rare disease AI teaches us about longitudinal health here:
https://myaether.live/blog/rare-disease-ai-longitudinal-health

Appreciate the PH community engaging with this perspective.

Aether Admin
Maker

One of the ideas shaping Aether comes from longitudinal symptom research in oncology.

A recent JCO Clinical Cancer Informatics study shows that future cancer symptom severity can be predicted using sparse, irregular EHR nursing documentation, as long as symptom history is preserved over time.

The key insight is not the model. It is that learning only works when health systems stop throwing away longitudinal context.

I wrote a short reflection on what this teaches us about health graphs and EHR Lite design here:
https://myaether.live/blog/predicting-cancer-symptom-trajectories-longitudinal-ehr

Appreciate the PH community engaging with this perspective.

Aether Admin
Maker

We’ve been thinking a lot about representation in healthcare AI.

A recent npj Digital Medicine paper built a multimodal sepsis embedding model that outperformed baseline models and even physicians in mortality prediction.

The deeper insight is not about sepsis. It’s about representation learning.

If admission-level embeddings can unlock this much signal, what happens when AI has longitudinal context across years?

We wrote a breakdown here:


https://myaether.live/blog/sepsis-ai-representation-longitudinal-future

Would love thoughts from the PH community.