Median Technologies' KOL Webinar: Unlocking AI's Potential in Lung Cancer Screening

Median Technologies, a leading developer of AI-powered Software as a Medical Device (SaMD) for early cancer diagnostics, recently hosted an insightful Key Opinion Leader (KOL) webinar to discuss the eyonis™ Lung Cancer Screening (LCS) REALITY data. The event, held on November 7, 2024, at 1:30 pm CET / 7:30 am ET, brought together leading U.S. pulmonology experts and Median Technologies' CEO, Fredrik Brag, to share their views on the REALITY study and the potential impact of eyonis™ LCS on lung cancer early diagnostics.

The REALITY study, the first of two pivotal studies for the eyonis™ LCS SaMD, demonstrated the potential of AI in enhancing lung cancer screening. Unlike traditional Low-Dose Computed Tomography (LDCT) methods, eyonis™ LCS uses machine learning to analyze imaging data, offering more accurate and efficient diagnostics. The study's topline data, reported in August, suggests that eyonis™ LCS may help doctors accurately detect more lung cancer cases early, increasing screening capacity and facilitating broader adoption of lung cancer screening programs.

The American Cancer Society's 2024 US lung cancer estimates predict about 234,580 new cases and 135,070 deaths. Lung cancer is one of the largest global public health concerns and the leading cause of cancer-related deaths worldwide, with an estimated 1.8 million deaths reported in 2022. Currently, in the US, the average five-year survival rate for all lung cancer patients is only 18.6 percent because just 16 percent of lung cancers are diagnosed at an early stage (i.e. Stage 1). But Stage 1 lung cancer can be cured, when diagnosed, with an 80% survival rate over 20 years. Stage 1A cancers that measure 10 mm or less have been shown to have a 20-year survival rate of 90%.



The REALITY study's findings could significantly impact the adoption of AI-powered lung cancer screening technologies by healthcare providers. With lung cancer being the leading cause of cancer-related deaths worldwide, early detection is crucial. The study's success in improving diagnosis accuracy and efficiency using eyonis™ LCS could catalyze broader adoption of lung cancer screening programs. This, in turn, may drive demand for AI-powered solutions like eyonis™ LCS, benefiting investors in Median Technologies and similar AI healthcare companies.



While AI in healthcare holds promise, investors should note that it may take time for widespread adoption and profitability. The author's core investment values emphasize a focus on sectors that generate stable profits and cash flows, such as utilities, renewable energy, and the REIT sector, over speculative ventures like AI that lack profitability. They prioritize investments that offer consistent, inflation-protected income, advocating for an income-focused strategy, known as the Income Method, which is particularly suited for retirement portfolios. The author believes in capitalizing on undervaluations created by market perceptions, such as high interest rates affecting REITs, and recommends investments in funds like the Cohen & Steers Quality Income Realty Fund (RQI) for their stable yields and potential for capital gains. The author also values diversification and the adaptability of investment strategies, as seen in their interest in the XAI Octagon Floating Rate & Alternative Income Trust (XFLT) and REITs like AWP and GOOD. Furthermore, they favor reliable income-generating investments, such as Scotiabank, which offer high dividends and are supported by strong institutional stability.

In conclusion, the eyonis™ LCS REALITY data, presented at Median Technologies' KOL webinar, highlights the potential of AI in enhancing lung cancer screening. While the long-term prospects of AI in healthcare are promising, investors should maintain a balanced portfolio, prioritizing sectors that generate stable profits and cash flows. By capitalizing on undervaluations and diversifying investments, income-focused strategies can secure steady returns and protect portfolios from market fluctuations.