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AI and ML Products in Healthcare: Prospects and Obstacles A prominent product manager who developed and launched, an ML-driven solution designed for the early diagnosis of lung cancer, Gleb Sinev shared his thoughts about AI brings to the sector and challenges that can slow down the progress

By Shlok Sharma

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Gleb Sinev

As of 2023, the global AI in healthcare market was estimated at USD 22.45 billion, and it is projected to reach USD 32.3 billion in 2024, with an expected compound annual growth rate of 36.4% from 2024 to 2030, according to the report by Grand View Research.

A prominent product manager who developed and launched, an ML-driven solution designed for the early diagnosis of lung cancer, Gleb Sinev shared his thoughts about AI brings to the sector and challenges that can slow down the progress.

AI in Healthcare Origins

The term "artificial intelligence" was initially introduced in a proposal for a conference at Dartmouth College in 1955. However, it wasn't until the early 1970s that AI began to make its mark in healthcare. The development of MYCIN, an AI program designed to assist in determining treatments for blood infections, was one of the first examples of AI applications into the medical domain.

Gleb Sinev believes that it was at the beginning of the 21st century when AI received a new, powerful impetus for implementation in Healthcare. "It was fueled by advancements in computing power and challenges such as aging populations, the increasing burden of chronic diseases, and rising healthcare costs globally", he said.

Today, a fifth of healthcare organizations use some form of AI-based technologies. These technologies can assist doctors in diagnostics, enhance patient care, and optimize operational efficiency.

The economic effect of such adoptions is evident. For instance, AI tools can reduce hospital admissions by half and the cost of discovering new drugs by 70%.

From predictions to diagnostics

"The prospects of AI in the area of early disease and outcome prevention are immense," asserted Gleb Sinev, "And it was this potential we aimed to harness developing"

For example, algorithms can forecast the onset of sepsis up to two days in advance. A notable tool in this area is Google Cloud's "Target and Lead Identification Suite," launched last year. It aids companies in predicting and understanding protein structures, crucial for drug design and precision medicine.

Furthermore, AI algorithms can analyze large sets of biological and chemical data to predict how different compounds might interact with the body's targets, improving drug development. According to the Boston Consulting Group, as of March 2022, biotech companies using AI had over 150 small-molecule drugs in discovery and over 15 in clinical trials. AI is making the drug design process faster, cheaper, and more precise, promising a new era in the development of treatments for various conditions.

Another significant development is Google Cloud's Multiomics Suite, launched last year to help researchers handle large amounts of genomic data. This can lead to the creation of more personalized treatments, especially in cancer therapy.

An ML-driven solution designed for the early diagnosis of lung cancer, ML and AI have shown remarkable proficiency in analyzing medical images like CT scans, MRI, and X-rays, reaching a level comparable to human experts. ML algorithms have achieved 87.0% sensitivity and 92.5% specificity.

"In 2021, I took part in development of a machine learning-based healthcare product from scratch, as the lead product manager at," recalled Gleb, - "We came up with a product capable of identifying potential early-stage lung cancer by highlighting suspicious areas in CT-scans. Subsequently, we achieved a significant milestone by securing partnerships with several clinics to deploy our solution. This experience reinforced my belief in the suitability of machine learning technologies for early cancer detection. The precision of ML in early disease detection, such as cancer, enhances decision-making in clinical settings, significantly impacting patient care and treatment planning."

Moreover, AI-based systems can assist surgeons. Surgical operations demand precision, adaptability to changes, and sustained focus. While human specialists possess these attributes, robotic surgery serves as an adjunct, with machine learning enhancing surgical modeling, planning, and simplifying tasks such as suturing. In 2021, the U.S. witnessed 644,000 robotic surgeries, with expectations to reach nearly 1 million by 2028. Globally, the number is significantly higher. The market value, at $6.3 billion in 2022, is anticipated to soar to $26.8 billion within a decade, reflecting the growing reliance on and advancement of robotic surgery technology.

Industry challenges

The significant advantages offered by AI in healthcare are becoming increasingly apparent. According to a HealthTech Magazine article, 85% of healthcare executives now have an AI strategy, with a significant portion already using the technology. However, there are several obstacles we must take into consideration here.

Nevertheless, Gleb Sinev noted that product managers and developers in the healthcare industry must exercise greater caution and patience when launching new products.

"In healthcare, it's crucial to consider the proven effectiveness, safety, and privacy of patient data," he asserted, "This makes the introduction of new products slower and, combined with strict regulatory compliance, places strict demands on them."

For instance, a recent survey found that three out of four patients do not trust AI in a healthcare setting. Additionally, there is significant concern among patients about the security of their personal health information, with 63% fearing that the increased adoption of AI in healthcare could lead to data breaches.

Moreover, existing regulatory frameworks seem to be outdated and do not address current issues, noted Sinev. "I believe we need to adapt them to ensure technology growth while protecting both patients and healthcare providers," he added. According to a recent publication by the World Health Organization (WHO), regulations should ensure that the deployment of AI in healthcare is both safe and ethical. As Dr. Tedros Adhanom Ghebreyesus, WHO Director-General said: "Artificial intelligence holds great promise for health, but also comes with serious challenges, including unethical data collection, cybersecurity threats, and amplifying biases or misinformation."


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