Leveraging artificial intelligence for advancements in reproductive health
Vaageessan Masilamani,
Masilamani V,
Bharathyuvaraj R,
Ragothaman Bharathyuvaraj,
Velayudam Ramalingam Elangovan,
Elangovan VR,
Ramji H,
Hema Ramji,
Mohanraj Subramanian,
Subramanian M,
Saravanan M,
Murthy Saravanan,
Jothimani D,
Devaraj Jothimani,
Moses F,
Fenn Moses,
Megalai M,
Mani Megalai,
Venu Gopal,
Gopal V,
Duraivel S,
Samuel Duraivel
other
OA: gold
public-domain-us
⚙
AI-generated summary
by gemini-2.5-flash-lite, 2026-06-13
ⓘ
Artificial intelligence is advancing reproductive health by enabling accurate prediction of fertility, optimizing IVF, and early detection of disorders through analysis of biomarkers and patient data.
⚙
AI-generated deep summary
by gpt-5.6-luna, 2026-08-12
· read from full text
ⓘ
This paper is a brief perspective on how artificial intelligence could advance reproductive healthcare through machine learning, big-data analytics, and analysis of biomarkers, medical histories, and lifestyle factors. It describes potential applications including predicting fertility outcomes, improving in vitro fertilization success rates, detecting reproductive disorders earlier, and supporting personalized treatment, particularly in underserved communities. The paper provides no study population, original methods, quantitative results, or explicitly stated limitations, functioning instead as a general overview of AI’s prospective clinical role. Relevance to endometriosis: the paper cites endometriosis as one reproductive disorder that AI tools may help identify, though the paper’s main focus is the broader application of AI across reproductive health.
Abstract
We are writing to address the growing interest in the role of artificial intelligence (AI) within healthcare, particularly in the field of reproductive health. As technology continues to evolve, AI offers an unprecedented opportunity to transform how we diagnose, treat, and improve access to reproductive services, especially in underserved communities. AI-driven tools, supported by machine learning and big data analytics, are already demonstrating their potential in enhancing outcomes in reproductive health. These tools can predict fertility outcomes with impressive accuracy, optimize in vitro fertilization (IVF) success rates, and identify early signs of reproductive disorders, such as endometriosis, polycystic ovary syndrome (PCOS), and ovarian cancer. By analyzing biomarkers, medical histories, and lifestyle factors, AI algorithms empower healthcare providers to deliver personalized and effective treatment plans tailored to individual needs.
Full text
1,099 characters
· extracted from
oa-doi-fallback
· click to expand
Leveraging artificial intelligence for advancements in reproductive health
Abstract
We are writing to address the growing interest in the role of artificial intelligence (AI) within healthcare, particularly in the field of reproductive health. As technology continues to evolve, AI offers an unprecedented opportunity to transform how we diagnose, treat, and improve access to reproductive services, especially in underserved communities. AIdriven tools, supported by machine learning and big data analytics, are already demonstrating their potential in enhancing outcomes in reproductive health. These tools can predict fertility outcomes with impressive accuracy, optimize in vitro fertilization (IVF) success rates, and identify early signs of reproductive disorders, such as endometriosis, polycystic ovary syndrome (PCOS), and ovarian cancer. By analyzing biomarkers, medical histories, and lifestyle factors, AI algorithms empower healthcare providers to deliver personalized and effective treatment plans tailored to individual needs.
Full Text:
PDFRefbacks
- There are currently no refbacks.
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.
My notes (saved in your browser only)
⚙
Ask this paper
AI returns verbatim quotes from the full text
· source: oa-doi-fallback
ⓘ
Condition tags
endometriosis
MeSH descriptors
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Citation neighborhood
(no data yet)
We don't have any in-corpus citations linked to this paper yet.
This is a recent paper (2024) — citers
typically take a year or two to land, and the OpenAlex reference
graph may still be filling in.
Source provenance
- europepmc
- last seen: 2026-08-19T06:08:28.815611+00:00
- pubmed
- last seen: 2026-08-19T06:05:58.654216+00:00
- unpaywall
- last seen: 2026-05-11T08:34:28.763810+00:00
License: public-domain-us
· commercial use OK
· attribution required
Courtesy of the U.S. National Library of Medicine