{"id":603391,"date":"2026-08-24T19:06:27","date_gmt":"2026-08-24T19:06:27","guid":{"rendered":"https:\/\/www.newsbeep.com\/ie\/603391\/"},"modified":"2026-08-24T19:06:27","modified_gmt":"2026-08-24T19:06:27","slug":"patients-are-bringing-ai-diagnoses-to-fertility-clinics-doctors-say-ai-can-explain-but-not-decide-the-south-first","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ie\/603391\/","title":{"rendered":"Patients are bringing AI diagnoses to fertility clinics. Doctors say AI can explain, but not decide – The South First"},"content":{"rendered":"

Many couples now visit the fertility clinic after already asking AI about their AMH, semen analysis, follicular scan, endometriosis, PCOS, IVF success rates or embryo quality.
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Published Aug 23, 2026 | 9:36 AM \u268a Updated Aug 23, 2026 | 9:36 AM<\/p>\n


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AI is a useful first layer of information, but not the final layer of decision-making.<\/p>\n

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Synopsis: AI is increasingly becoming a private first stop for men with fertility and sexual-health concerns, offering anonymity and instant answers without the embarrassment of speaking to a doctor. This story examines whether ChatGPT and other AI tools are helping a person to understand infertility or encouraging self-diagnosis, false reassurance and delays in seeking specialist care.<\/p>\n

A woman reads her Anti-Mullerian Hormone (AMH) result\u2014a test that measures a blood protein produced by cells in ovarian follicles to estimate a woman\u2019s remaining egg supply, or ovarian reserve\u2014 and concludes she cannot conceive naturally.<\/p>\n

A man sees one abnormal semen parameter and concludes he is infertile. Increasingly, fertility specialists are meeting patients who arrive at the clinic with conclusions formed before a consultation.<\/p>\n

Dr Ramya MR, Consultant in Reproductive Medicine and Infertility at Rainbow Children\u2019s Hospital, Chennai, said patients were increasingly arriving after using AI to interpret AMH results, semen analysis, follicular scans, IVF success rates and embryo quality. Dr Krishna Chaitanya, Clinical Embryologist at Oasis Fertility, Hyderabad, had also seen patients bring AI-generated explanations into consultations, and worries the technology\u2019s reach into fertility care is only starting.<\/p>\n

\u201cMany couples now come to the fertility clinic after already asking AI about their AMH, semen analysis, follicular scan, endometriosis, PCOS, IVF success rates or embryo quality,\u201d Dr Ramya told South First. \u201cThe major change is that patients are no longer coming only with questions, some come with a presumed diagnosis.\u201d<\/p>\n

She mentioned the questions that followed. \u201cMy AMH is 0.8, how many eggs will I get?\u201d one patient asked. \u201cMy morphology is 2%, can I become a father?\u201d asked another. A third wanted to know if a top-graded embryo guaranteed a pregnancy.<\/p>\n

\u201cAI can explain what these numbers mean, but fertility cannot always be predicted mathematically,\u201d she said. Two patients with identical lab values can face very different outcomes depending on age, ovarian response, tubal status, uterine factors and how long they have been trying to conceive.<\/p>\n

Also Read: How masculinity shapes male infertility in India<\/a><\/p>\n

A tool that explains numbers but not people<\/p>\n

Dr Ramya separated what AI does well from what it cannot do. \u201cAI is very useful for basic education,\u201d she said. \u201cIt can explain ovulation, the fertile window, AMH, semen parameters, PCOS, endometriosis and the basic differences between IUI, IVF and ICSI.\u201d<\/p>\n

The gap opens once a couple\u2019s actual case needs a decision. \u201cClinical judgement becomes essential when deciding why a couple is not conceiving and what should be done next,\u201d she said. That decision depends on a woman\u2019s age, ovarian reserve, semen parameters, tubal status, uterine pathology and treatment history, factors an AI tool cannot examine or weigh against each other. \u201cAI can interpret individual pieces of information. A fertility specialist has to connect all those pieces and decide what is clinically relevant.\u201d<\/p>\n

Semen analysis draws the sharpest misreadings, Dr Ramya said. Patients fixate on a single figure and treat it as a final answer.<\/p>\n

\u201cOne of the commonest fears we hear is, my morphology is low, so I can never have a child, or my sperm count is below normal, so IVF is the only option,\u201d she said. \u201cNeither conclusion should be made from one isolated parameter.\u201d<\/p>\n

A semen report needs to be read alongside the abstinence period before the sample, the collection method, laboratory methodology, medical history and often a repeat test, since normal biological variation can shift the numbers between samples. \u201cTreatment decisions should be based on the couple as a whole, not merely on one semen report.\u201d<\/p>\n

Also Read:\u00a0Can lenses slow a child\u2019s worsening eyesight?<\/a><\/p>\n

When the prescription itself comes from AI<\/p>\n

Dr Chaitanya\u2019s concern extends beyond patients reading their own reports. He has watched general practice move toward a stage where AI does not just explain a diagnosis but writes the treatment.<\/p>\n

\u201cMultiple doctors in different practices, mostly in general practice, have seen patients getting prescriptions from the AI,\u201d he said to South First. \u201cI\u2019m not sure how much distance it has travelled to my practice, but I can see it is in the profession as well in different forms. They get tests done, put the report and then come ask for the prescription or say that the AI chatbot has said something else.\u201d<\/p>\n

That distinction matters. A patient reading an AI explanation of a lab report is one problem, but getting a solution is a different matter altogether.<\/p>\n

The problem, Dr Chaitanya said, was not patients reading AI-generated information. It was treating that information as a final medical judgement.<\/p>\n

\u201cIt\u2019s okay to get information,\u201d he said. \u201cBut it\u2019s not okay to reach a conclusion, and believe that what the doctor\u2019s saying is wrong, and you don\u2019t want to agree with that, but you want to agree with AI.\u201d He put the underlying test: \u201cThe patient needs to understand that you must read it but not believe it.\u201d<\/p>\n

He does not object to patients citing AI in his consultation room, only to what they expect him to do with it. \u201cI don\u2019t mind if you come and say, I read this on ChatGPT, and I believe this could be a problem,\u201d he said. \u201cBut allow me to explain. You can\u2019t now tell me, since ChatGPT has said this, you also say this.\u201d<\/p>\n

Also Read: India\u2019s booming medical AI sector risks safety without crucial clinical validation<\/a><\/p>\n

Privacy that helps\u2014and delays<\/p>\n

Male infertility carries a stigma that keeps many men from raising the subject at all, and Ramya sees AI opening a door that embarrassment usually keeps shut. \u201cMale infertility still carries significant stigma, and many men hesitate to openly discuss sperm count, erectile difficulties, ejaculation problems, sexual frequency or lifestyle factors,\u201d Dr Ramya said. \u201cAI provides a private space where they can first understand these topics without embarrassment.\u201d<\/p>\n

She framed that privacy as a genuine benefit, not just a workaround. \u201cThat can actually be beneficial because it may encourage men to participate more actively in fertility evaluation rather than considering infertility only as a woman\u2019s problem.\u201d<\/p>\n

But she is precise about where that benefit ends. \u201cThis anonymity should ideally be the beginning of the conversation, not the end,\u201d she said. \u201cAny concern identified should ultimately be discussed with a fertility specialist for proper evaluation.\u201d<\/p>\n

When reassurance costs time<\/p>\n

Dr Ramya\u2019s sharpest warning concerns what AI cannot factor in: time. Fertility treatment often depends on age and diagnosis in ways a general-purpose model has no way to weigh correctly for an individual case.<\/p>\n

\u201cA 28-year-old woman trying for six months and a 39-year-old woman trying for the same duration cannot necessarily be given the same advice,\u201d she said. Conditions such as severe male-factor infertility, blocked fallopian tubes, advanced endometriosis or significantly reduced ovarian reserve can call for earlier treatment rather than more waiting. \u201cIf AI repeatedly reassures a patient without understanding these factors, valuable reproductive time can be lost.\u201d<\/p>\n

She described where AI fits in fertility care, and where it stops. \u201cI would describe AI as a very useful first layer of information, but not the final layer of decision-making,\u201d she said. \u201cAI can explain the report, but it cannot examine the patient, understand the couple\u2019s complete reproductive history, integrate all the investigations and decide the right treatment pathway. That remains the responsibility of the fertility specialist.\u201d<\/p>\n

(Edited by Majnu Babu).<\/p>\n

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