Comments on: AI-Assisted Prakriti Assessment: How Machine Learning Is Reshaping Dosha Testing

By: Mark Bennett

Mark Bennett — Tue, 15 Sep 2026 02:40:47 +0000

It’s interesting to see the genomic validation linking algorithmic Prakriti types to CYP gene expression, suggesting some biological basis.

By: Jessica Reed

Jessica Reed — Thu, 03 Sep 2026 07:25:26 +0000

A question that comes up is whether a single image can truly capture the lifelong constitutional traits described in the Charaka Samhita.

By: Rohan Yadav

Rohan Yadav — Thu, 27 Aug 2026 15:30:42 +0000

@Anna commercializing Prakriti assessment through AI apps risks reducing a nuanced diagnostic tradition to a wellness product. the commodification concern is real. धन्यवाद

By: Rebecca Turner

Rebecca Turner — Fri, 21 Aug 2026 23:16:56 +0000

Practitioners might appreciate knowing that the current models are trained mainly on South Asian data, which could limit use elsewhere.

By: Laura Mitchell

Laura Mitchell — Mon, 10 Aug 2026 02:06:06 +0000

I wonder how the multi modal fusion model handles Vikriti versus Prakriti distinction.

By: Anjali Tiwari

Anjali Tiwari — Sun, 02 Aug 2026 20:20:00 +0000

@Tim the machine learning approach to dosha pattern recognition from self-reported symptom clusters is genuinely interesting. as an ML engineer, the feature selection approach makes technical sense.

By: Indira Rao

Indira Rao — Sun, 02 Aug 2026 09:00:52 +0000

Doing this.

By: Chris Allen

Chris Allen — Sat, 01 Aug 2026 16:11:06 +0000

In reply to David Walker. That's a bit of a different topic from the article, but to your question — I've actually found the article's point about ML handling borderline Pitta-Kapha cases more relevant here, because PCOS often presents with mixed dosha involvement and the diagnostic ambiguity is the same kind of challenge.

By: Sunita Pandey

Sunita Pandey — Fri, 31 Jul 2026 15:46:02 +0000

Thanks!

By: David Walker

David Walker — Thu, 30 Jul 2026 11:40:10 +0000

The speed element is what stands out most to me. A senior practitioner taking 30 minutes to reach the same conclusion as a model doing it in seconds — does that compression of time change anything about how the patient receives or trusts the diagnosis?