Published Aug 23, 2026 | 9:36 AM ⚊ Updated Aug 23, 2026 | 9:36 AM
AI is a useful first layer of information, but not the final layer of decision-making.
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.
A woman reads her Anti-Mullerian Hormone (AMH) result—a test that measures a blood protein produced by cells in ovarian follicles to estimate a woman’s remaining egg supply, or ovarian reserve— and concludes she cannot conceive naturally.
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.
Dr Ramya MR, Consultant in Reproductive Medicine and Infertility at Rainbow Children’s 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’s reach into fertility care is only starting.
“Many 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,” Dr Ramya told South First. “The major change is that patients are no longer coming only with questions, some come with a presumed diagnosis.”
She mentioned the questions that followed. “My AMH is 0.8, how many eggs will I get?” one patient asked. “My morphology is 2%, can I become a father?” asked another. A third wanted to know if a top-graded embryo guaranteed a pregnancy.
“AI can explain what these numbers mean, but fertility cannot always be predicted mathematically,” 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.
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Dr Ramya separated what AI does well from what it cannot do. “AI is very useful for basic education,” she said. “It can explain ovulation, the fertile window, AMH, semen parameters, PCOS, endometriosis and the basic differences between IUI, IVF and ICSI.”
The gap opens once a couple’s actual case needs a decision. “Clinical judgement becomes essential when deciding why a couple is not conceiving and what should be done next,” she said. That decision depends on a woman’s age, ovarian reserve, semen parameters, tubal status, uterine pathology and treatment history, factors an AI tool cannot examine or weigh against each other. “AI can interpret individual pieces of information. A fertility specialist has to connect all those pieces and decide what is clinically relevant.”
Semen analysis draws the sharpest misreadings, Dr Ramya said. Patients fixate on a single figure and treat it as a final answer.
“One 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,” she said. “Neither conclusion should be made from one isolated parameter.”
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. “Treatment decisions should be based on the couple as a whole, not merely on one semen report.”
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Dr Chaitanya’s 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.
“Multiple doctors in different practices, mostly in general practice, have seen patients getting prescriptions from the AI,” he said to South First. “I’m 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.”
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.
The problem, Dr Chaitanya said, was not patients reading AI-generated information. It was treating that information as a final medical judgement.
“It’s okay to get information,” he said. “But it’s not okay to reach a conclusion, and believe that what the doctor’s saying is wrong, and you don’t want to agree with that, but you want to agree with AI.” He put the underlying test: “The patient needs to understand that you must read it but not believe it.”
He does not object to patients citing AI in his consultation room, only to what they expect him to do with it. “I don’t mind if you come and say, I read this on ChatGPT, and I believe this could be a problem,” he said. “But allow me to explain. You can’t now tell me, since ChatGPT has said this, you also say this.”
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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. “Male infertility still carries significant stigma, and many men hesitate to openly discuss sperm count, erectile difficulties, ejaculation problems, sexual frequency or lifestyle factors,” Dr Ramya said. “AI provides a private space where they can first understand these topics without embarrassment.”
She framed that privacy as a genuine benefit, not just a workaround. “That can actually be beneficial because it may encourage men to participate more actively in fertility evaluation rather than considering infertility only as a woman’s problem.”
But she is precise about where that benefit ends. “This anonymity should ideally be the beginning of the conversation, not the end,” she said. “Any concern identified should ultimately be discussed with a fertility specialist for proper evaluation.”
Dr Ramya’s 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.
“A 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,” 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. “If AI repeatedly reassures a patient without understanding these factors, valuable reproductive time can be lost.”
She described where AI fits in fertility care, and where it stops. “I would describe AI as a very useful first layer of information, but not the final layer of decision-making,” she said. “AI can explain the report, but it cannot examine the patient, understand the couple’s complete reproductive history, integrate all the investigations and decide the right treatment pathway. That remains the responsibility of the fertility specialist.”
(Edited by Majnu Babu).
