In the ever-evolving world of healthcare, the cost-effectiveness ratio is the rough landscape upon which the ball of progress rolls. A prominent healthcare leader, Alexandru Floares (Founder, and CEO of Artificial Intelligence Expert), describes this scenario in detail. He explains, “It is a landscape full of mountains and valleys. Mountains represent high costs and low efficacy on this terrain, whereas valleys represent low costs and high efficacy.” The pushing force behind the ball’s progress is the advancements in ICT (Information and Communication Technology). Now the “ball” is climbing a mountain of high costs and low efficiency, making it unaffordable to reach countries. However, the ascent is slower and harder than expected, owing to insufficient and fragmented ICT/AI tools and patient data. In addition, despite patient-centric goals, a lack of focus on “helping doctors help their patients” has resulted in physician burnout and medical errors.
Alexandru further explains, as the ball can only move from one valley to another by traversing a mountain, which means going up the mountain is unavoidable. It is a necessary stage, and the industry must remember to take into account the triad of patients, physicians, and businesses. While the first two are well understood, the third is crucial because solutions that are perfect for patients and physicians but unaffordable will not be adopted.
A Paradigm Shift
Alexandru clarifies the shift in healthcare in a simple way. The previous stable state of healthcare was dominated by anatomy, pathology, statistics, and disease detection by invasive methods, with surgery as the primary treatment. However, the rise of molecular biology, radiology, and artificial intelligence has altered the healthcare landscape. Early non-invasive detection, chemotherapy, and robotic surgery have become the norm in this setting. The physicians will learn to collaborate with AI as it automatically follows the best practice. Robotic surgery, in particular, will be quickly adopted as it is helpful and easy to use.
Progress will be inevitable as the ball continues to roll up the mountain of healthcare costs. According to Alexandru, a former neurologist turned AI advocate, the industry will eventually reach the other side, where valleys of low costs and high efficacy await.
Bringing Data Science approach to the Medical World
In 2017, Alexandru founded Artificial Intelligence Expert, as a spinoff of SAIA, a private research organization that focused on applying AI in healthcare, particularly in oncology.
The mission of AIE is clear and concise – To improve cancer outcomes and save lives by providing accessible and accurate non-invasive cancer detection and comprehensive biomedical data analysis.
Its vision is equally ambitious – To become the leading provider of innovative cancer solutions, advancing early detection and precision medicine for all.
With a focus on innovation and exclusive services, AIE has the potential to change the way cancer is detected and treated. One of its greatest achievements is the non-invasive Multi-Cancer Early Detection (MCED) test. Using advanced AI, the test recently achieved a robust accuracy of 100%, a result so impressive that they initially believed something was wrong. However, Alexandru believes, it could achieve maximum performance using different alternative methods with the same strategy. Additionally, the price of the test is affordable, and it uses widespread PCR technology, making it challenging for the competition to beat. Therefore, Alexandru and his team’s comprehensive approach is giving hope to millions of people worldwide who are affected by this devastating disease.
AI-Powered Services and Solutions
In healthcare, extracting insights from high-throughput data is critical for improving patient outcomes. However, this is no easy feat. As the industry generates a massive amount of high-throughput data, it is difficult to interpret without the use of Data Science and an AI approach. This is where the AIE steps in. The crucial difference between its solutions and those of competitors is that it strives to help doctors understand the paradigm shift in high-throughput data analysis. In contrast, the competitors usually show how smartly they could apply the old paradigm.
Alexandru states, most studies are costly and just a list of differentially expressed omics rather than extracting all information from data. This is merely an optional exploratory data analysis intermediary step. However, the company believes the relationship between the biomarkers and the output is equally important. Predictive models derived from data by AI can aid in this, and the company does not consider differentially expressed omics to be the end result. This is due to the fact that no AI/ML algorithm can predict based on omics that do not distinguish between two or more biomedical situations. Therefore, these uninformative omics are eliminated.
Another important aspect is that living systems have high functional redundancy at the molecular level. This means that different molecular processes can realize the same cellular function, leading to an impact on biomarker discovery. Therefore, aggressive feature selection may result in poor generalization to new cases.
Multi-Cancer Early Detection (MCED) test by AIE leverages the power of advanced AI to overcome these complications. They recently reached a robust accuracy of 100% which empowers this solution to transform cancer detection and treatment.
