As assisted reproductive technologies rapidly integrate artificial intelligence, automation and advanced genetic tools, it is important to understand which innovations have genuinely improved patient outcomes and which primarily enhance operational efficiency. Conversations with clinicians who have witnessed IVF’s evolution over several decades provide critical perspective on how technology, clinical judgment, and laboratory discipline together shape success in reproductive medicine. In this exclusive interview with Dr. Umesh N. Jindal, Director & Senior Consultant at Jindal IVF, Dr Asawari Savant from Elets News Network (ENN) explores the long-view, through the experience of Jindal IVF, also traced how key technological milestones have translated into measurable gains for patients. Edited excerpts

In your over three decades of clinical experience, which IVF technologies have evolved from the late 1980s to 2026, and which technological shifts delivered maximum improvements in patient outcomes over time? 

When I started my practice in the late 1980s IVF depended on basic culture media, manual embryo observation, and limited laboratory control, which kept success rates modest and unpredictable, particularly for women with hormonal, ovulatory, and tubal infertility. Advances in ovarian stimulation and cycle monitoring improved outcomes for female infertility, while the introduction of ICSI changed results for severe male infertility. Blastocyst culture improved timing and embryo selection. Vitrification transformed embryo and oocyte freezing from a risky step into a reliable strategy, allowing flexibility and cumulative success. Time-lapse incubators enabled continuous embryo monitoring without disturbance, improving selection accuracy. Preimplantation genetic testing shifted the goal from achieving pregnancy alone to ensuring healthy births. Today, artificial intelligence assisted embryo grading, advanced air quality systems, and personalised stimulation protocols refine every stage of care. These shifts delivered measurable gains through higher implantation rates, lower miscarriage risk, and fewer treatment cycles for families.

What automation, AI-based imaging, and digital lab systems have changed inside embryology labs, and which stages still rely primarily on clinician and embryologist judgment?

In modern laboratory, I have seen technology slowly take over tasks that once needed constant human supervision. Time-lapse incubators now observe embryos around the clock, AI-based imaging helps interpret growth patterns more objectively, and digital witnessing systems protect identity at every step. Automated environments keep air quality and temperature stable, while digital records preserve the full story of each cycle. Even so, the most important choices still rest with clinicians and embryologists. We at Jindal IVF depend on clinical judgment to design stimulation plans rather than software alone. Subtle embryo behaviour is interpreted through trained observation, not only numerical scores. Decisions on transfer timing come from patient history and real cycle response, not predictive models. And when couples seek guidance, the conversation is led by empathy and understanding, not algorithms. Technology strengthens accuracy and safety, but it is human experience that gives each decision meaning, context, and responsibility.

How Jindal IVF has evaluated technologies such as ICSI, PGT, vitrification, and successive lab upgrades over long timelines to distinguish clinical value from operational efficiency?

In our practice, new technology has never been adopted for novelty but for demonstrable biological impact. ICSI was introduced after validating fertilisation rates, embryo morphology, and pregnancy outcomes in severe male factor cases against conventional IVF. PGT was evaluated through longitudinal tracking of aneuploidy rates, implantation, and miscarriage reduction rather than diagnostic accuracy alone. Vitrification was accepted only when post thaw survival, blastocyst viability, and cumulative live birth rates equalled or exceeded fresh transfer benchmarks. Each laboratory upgrade was audited for effects on culture stability, contamination control, and inter cycle reproducibility, not just throughput. Technologies that improved workflow without improving embryo competence were treated as operational tools, not clinical advances. Distinguishing value from efficiency has meant analysing outcome data over years, not weeks, and privileging measurable gains in embryo viability and live birth over procedural speed.

How increasing lab sophistication has altered patient workflows, clinician involvement, and continuity of care across multiple treatment cycles?

When reproductive medicine began, each IVF cycle stood largely on its own, with limited ability to learn from what came before. Today, laboratory sophistication has transformed treatment into a connected and cumulative pathway. Culture platforms, vitrification systems, and genetic testing now allow embryos and clinical data to extend safely across cycles instead of being confined to a single attempt. Patient workflows are organised with fewer disruptions because stimulation, retrieval, culture, and transfer are synchronised through digital tracking and real time laboratory feedback. Clinician involvement has become more analytical rather than distant, guided by embryo development patterns, endocrine response, and prior cycle performance rather than guesswork. Continuity of care is stronger because every cycle builds on recorded biological behaviour, allowing protocols to evolve intelligently instead of restarting blindly.

How data systems, electronic records, and success rate reporting are used to guide clinical decisions without oversimplifying a probabilistic science

In our work, numbers are treated as guides, not verdicts. Electronic records allow every hormone profile, embryo parameter, and previous outcome to be viewed as part of a biological pattern rather than an isolated statistic. Success rate reporting is analysed in layers by age, diagnosis, embryo stage, and technique so probability is contextualised instead of averaged into a single promise. Data systems help identify trends in stimulation response or embryo competence, but final decisions are shaped through clinical reasoning that respects individual variation. A low probability does not mean futility, and a high probability does not remove uncertainty. Digital tools sharpen judgment, yet the science of reproduction remains probabilistic, requiring interpretation, counselling, and cautious optimism rather than mechanical prediction.

Also read: Standardising Egg Freezing: The Future of IVF Automation 

What three decades of technology adoption at Jindal IVF reveal that newer, tech-first IVF centres may underestimate or overlook?

When we founded Jindal IVF in 1989, little did we know that three decades of practice would teach lessons no textbook or machine could provide. Experience has shown that technology behaves differently once it meets real patients with complex biology, repeated failures, and emotional fatigue. New centres often underestimate how much outcomes depend on laboratory discipline, protocol consistency, and clinical memory built across thousands of cycles. Machines can create precision, but only long observation teaches when to trust a result and when to question it. Success is not one impressive cycle but sustained live births across age groups and diagnoses. Subtle shifts in culture conditions, stimulation response, and embryo behaviour become meaningful only when tracked over years. True progress in IVF is slow, cumulative, and guided by human judgment.

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