How AI Is Making Healthcare More Affordable And Accessible

Sahil Gupta is a physician by training and co-founder/Chief Commercial Officer at Oma Robotics, leading operations and business strategy.

Futuristic monitor with information data


Artificial intelligence is transforming how we practice medicine in a wide range of domains, including radiology, pathology, dermatology, surgery and infertility. AI algorithms are rapidly advancing to optimize processes such as image analysis, pattern recognition and data-based assessments, improving clinical workflows and the early detection and treatment of diseases.

AI is still an emerging technology in healthcare, but it shows incredible potential to make care more affordable and accessible for all patients. As a physician and co-founder of a company using fertility technologies powered by AI and robotics, I see how AI innovations are opening up new possibilities and access for patients from diverse backgrounds.

The main barriers to assisted reproductive technology are high costs, low success rates, complex treatment plans and lack of access. In the United States, approximately 13% of women aged 15 to 49 experience fertility problems. Yet a single in-vitro fertilization cycle can range from $15,000 to $30,000. IVF success rates vary depending on factors such as a patient’s age, lifestyle and health conditions, and many women need to go through multiple IVF cycles before bringing a pregnancy to term.

By using AI technologies, our clinics can now collect and analyze vast quantities of data, automate processes and make informed decisions about treatment protocols—which can significantly lower costs and improve success rates.


Here are three key ways AI is paving the way for healthcare to become more equitable and effective in the future.

1. Improving Data Quality

Using high-quality data in healthcare is critical because health outcomes affect human lives. Bad data input can lead to physicians making poor decisions, resulting in harmful—even fatal—outcomes for patients.


AI is changing the way we collect and employ data in healthcare. The better data we have, the better algorithms we can build for patients’ benefit. In embryology labs, for example, data is typically collected using a low-resolution camera, which limits an embryologist’s ability to select the most viable sperm for injection. My team and I knew we needed to collect meticulous, high-quality data to advance the data sets we use in our algorithm, so we upgraded our optics to capture higher-resolution images. As we achieve more granularity in our data and algorithm, our embryologists can make better decisions.

2. Finding Connections And Answers

AI technologies can process massive amounts of data and find connections that human brains simply can’t. So much raw data is generated in healthcare that, as physicians and researchers, we often don’t even know the right questions to ask. AI quickly analyzes large data sets and identifies patterns that can have a major impact on patient outcomes.

If we are able to detect anomalies and diagnose diseases more accurately, with reduced human intervention, then we can increase accessibility and affordability of care—particularly in underserved parts of the world. AI-powered microscopes, for instance, are being developed that can quickly and inexpensively inspect the margins of tumors or identify and count malaria parasites within minutes.

3. Automating Protocols

Healthcare often involves subjective decision-making processes, with physicians choosing treatment plans based on their own analysis of available information. AI can reduce subjectivity and create effective, standardized processes based on much larger data sets.

In the IVF process, patients must undergo a stimulation protocol, where they are injected with medication to develop follicles in their ovaries. Protocols vary depending on factors such as age, weight, follicular scans and image values. In the past, a physician would examine individual patient data and recommend an initial protocol, then switch to another course if necessary.

But with AI, we can automate much of this process and minimize trial and error. Patients fill in a questionnaire in an electronic medical record system, and an algorithm will report which stimulation protocol has worked best in the past for patients with similar demographics.

AI is not a replacement for human knowledge and expertise; on the contrary, it complements and augments how physicians treat patients. Physicians are sometimes slow adopters of new technology, resisting the idea that innovations could supersede their clinical decision-making skills. But I believe AI is a valuable tool that physicians should embrace for everyone’s benefit. AI is a helper, not a competitor. It learns rapidly, in a guided or autonomous setting, to reduce manual workload and help physicians deliver the best possible outcomes for all patients.

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