I was thinking, if this was the scenario some 10 years back, people would have thought AI in reproductive medicine means artificial insemination in reproductive medicine. But we have come a long way. IVF is a very young science, but it is the fastest growing science medical faculty. And within 40 years, it has become a 40-billion-dollar industry now. So, it is a huge market for AI. I would like to focus on those areas where AI is going to play a pivotal role and decisive role in reproductive medicine. And what the IVF in future would look like.
Understanding Artificial Intelligence
Artificial intelligence is a strong technological way providing the ability for a machine to perform the human brain function. Like perceiving, reasoning, learning and interacting. Broadly defined, AI here refers machine mimicking human decisions and tasks.
Machine Learning and Deep Learning
And machine learning is a subset of AI technology which uses statistical method to draw a conclusion or prediction model. So please continue. Take care of the time.
Why Data Matters
Because AI is zero without data. As database improves, data accessibility improves. Insights gained may lead to decision support tool and could guide the doctor or the patient whether to continue the IVF cycle or to cancel it. Now there is another subset of machine learning that is deep learning. It being a subset of AI allows computers to detect patterns from complex data sets. Huge data, even the CPU cannot manage that. GPU is required.
And then it makes predictions. Why ART? You can see. Limited success, we are stuck around 30%.
The live birth rates. Reliance on human expertise. Lack of personalisation.
High-cost ART. Invasive procedure. And there are some ethical concerns.
And with significant technological advancement, the success rate of IVF has notably increased. But implantation rate without PGT, it is around 50%. And with PGT, it has gone around. I'm talking about implantation rate, not live birth rate. It's around 60%. But as far live birth rate is concerned, it's typically hovering around 30, 32%. We are really stuck there. Indicating the challenges that persist despite advancement in IVF technology.
Why IVF Needs Artificial Intelligence
Now why ART is required? You see then, IVF being a sophisticated multi-stage procedure that utilises diverse resources, faces challenges due to its labour and time-intensive nature, coupled with significant inter- and intra-observer variability.These challenges impact reproducibility and the efficiency of ART. AI can solve that problem. As artificial intelligence becomes increasingly integrated into assisted reproductive technology, understanding these advancements is becoming essential for fertility specialists. Today, a Fellowship in IVF and Reproductive Medicine in India equips clinicians with the knowledge and practical skills needed to apply emerging technologies such as AI, machine learning, and data-driven decision-making in modern IVF practice.
Benefits of AI in Clinical Practice
AI has the potential to alleviate the workload of clinicians and embryologists. AI tools are quick and have a consistent standard in every IVF lab. AI not only guarantees optimal practises and results, but also reduces the likelihood of human error. Now you can see that how we apply in reproductive medicine. We get the data from electronic medical records, hospital data. Then there are machine learning and natural language processing. And in reproductive medicine, we use it in clinical practise and research and experiment.
Dr. Kamini Rao Hospitals, a popular IVF Centre in Bangalore, has always been open to technological advancements for the benefit of reproductive medicine. With the advent of artificial intelligence in assisted reproductive technology, Dr. Kamini Rao Hospitals continues to adhere to evidence-based practice to aid accurate diagnosis, optimize lab operations in embryology, and offer personalized treatments. The dedication of the institution towards using clinical expertise with technological advancements depicts how the future of IVF involves AI.
Clinical Applications
In clinical practise, we are using it for sperm cells, quality detection, oocyte, embryo, and cost effectiveness also. With machine learning, we are using only supervised, mainly supervised and unsupervised. Why AI in ART?
Towards Personalized Fertility Treatment
With rapid pace of computer and genomic science, the future of reproductive science is likely to be a personalised digital fingerprint or digitome embedded in patients' medical records.
With regards to pharmacogenomics, the genetic heterogeneity between each IVF patient is an opportunity to tailor IVF treatment specifically for that individual. Because we'll have the DNA fingerprinting. So, for example, FSH receptor polymorphism.
We know there are three varieties. Someone needs more, more dosage of FSH. But in some cases, it requires less. So that targeted treatment we can do. Startup companies in Israel, such as Fertility, Embryonics, and Alife in California are developing AI systems aimed at clinics which can integrate IVF workflow and offer end-to-end optimisation and cost savings balanced with the promise of personalisation of the treatment. We are striving for a personalised medicine.
With Medline Academics, there are plans of developing future leaders in the domain of fertility care in terms of providing them with the necessary knowledge and experience that they need to become successful and skilled practitioners of the quickly advancing science of reproductive medicine. With the advancement of artificial intelligence in different stages of the IVF process, from ovarian stimulation and embryo selection through personalized treatment and optimization of workflows, the requirement of clinicians capable of incorporating these technologies becomes greater than ever before. Using its Fellowship in IVF in India program, Medline Academics offers academic education supported by simulations, exposure to clinics, and mentorship to keep up with technology and provide precision in fertility care.
AI-Driven Workflow Optimisation
Again, workflow optimisation. If someone comes to an IVF lab, if some five, six cases are going on, in one day, you see that new sites are coming here. Then some consultant has to do some embryo transfer. Then some thawing has to be done. It's really a powerhouse. Then how to optimise the whole work schedule.
AI optimises the scheduling of the predicting peak times. When exactly it will be busy. Accordingly, you can arrange your staffs.