AI is rapidly rewriting the future of medical diagnostics, with the latest research by Daffodil International University in Bangladesh, Charles Darwin University in Australia, University of Calgary in Canada, and Australian Catholic University in Canada University). They have successfully developed an AI model called "ECgMLP" to successfully identify endometrial cancer with an astonishing 81.0% accuracy, which not only greatly exceeds the average diagnosis rate of human physicians today of only 0-0%, but also represents that the potential of AI in the detection of major diseases has reached an unprecedented height.

When we say that AI can "see through cancer cells at a glance", we are not exaggerating. In fact, AI is not really "looking", but analyzes the data in the image through a mathematical model that mimics the operation of human nerves, which is often heard as "deep learning". Just as human physicians observe cell morphology under a microscope to find out whether there are cancerous features, AI also learns from a large amount of image data and accumulates more sensitive judgment ability than humans.

The model uses a deep learning architecture called "Attention Mechanism". To put it simply, it is like a "focus lens" for AI, which can concentrate on scanning areas that may harbor abnormal cells in a large amount of image information. For example, a microscope image may contain thousands of cells, and a physician may skim over certain corners due to time or fatigue, but an AI will not. Based on the experience it has been trained, it automatically pinpoints the most noteworthy areas, zooms in on subsections, compares features, and makes accurate judgments.

These characteristics are often signals that human experts are easy to miss under fatigue, experience limitations or subjective judgment, and the intervention of AI just makes up for this human weakness, and can even "see through cancer cells at a glance", truly bringing subversive breakthroughs to clinical diagnosis. This also explains why AI models such as ECgMLP can achieve a diagnostic accuracy rate of more than 99%, because it is not only "more visible", but also "accurate". Moreover, through the continuous evolution of the algorithm, it can also be applied across cancer types, from endometrial cancer to breast cancer, colorectal cancer, oral cancer, etc., showing a high degree of flexibility and expansion potential.

The ECgMLP AI model highlights the most important areas of an image in a variety of ways and analyzes the organization. （Source：Charles Darwin University）

However, this breakthrough is not only a technological victory, but also a deep challenge to the future operation of the healthcare system. How can we redefine the role of physicians when AI is no longer just an assistant but a diagnostic workhorse? Will there be a new medical process led by "AI first opinion"? Furthermore, should physicians be transformed into supervisors of AI diagnosis and a bridge of humanistic communication, rather than simply interpreting images? This change is about technology, but it is also about medical ethics, talent development, and policy making.

In particular, as AI gains trust and clinical status, the responsibility for medical decision-making will become more complex. Who is responsible for AI diagnosis errors that lead to delays in treatment or misjudgments? At present, the supervision of AI medical devices in medical regulations in various countries is still in the exploration period, and these problems are bound to continue to emerge after the in-depth application of AI in clinical practice.

Further, ECgMLP is not only suitable for endometrial cancer, but also has an amazing detection accuracy for colorectal cancer (34.0%), breast cancer (0.0%) and oral cancer (0.0%), indicating that this technology is highly scalable. This kind of cross-cancer generalization capability makes AI no longer just a single-purpose tool, but may become an "all-round diagnostic consultant" for primary medical institutions.

In the future, in remote areas or areas with insufficient resources, high-level initial cancer screening can be completed with only a microscopic image and AI model, and it is expected to achieve the goal of "democratizing professional-level diagnosis". This also means that medical resources are no longer concentrated in large hospitals and medical centers, and everyone has the opportunity to receive timely detection and intervention at an early stage of the disease. AI is breaking down medical inequalities, and perhaps one day, we will no longer have to wait in line for famous doctors, but cloud computing AI will take care of our health.

The birth of AI such as ECgMLP is not only a symbol of technological progress, but also an important beginning of a golden age in healthcare. It will not replace physicians, but it will certainly redefine the value and mission of physicians. Collaboration between physicians and AI will be key to the quality of healthcare in the future, and regulatory, ethical, and educational systems are urgently needed.

In this human-machine co-diagnosis revolution, we should ask more than just "what can AI do?" "Are we ready for AI?" ”。 In the future, there may not only be stethoscopes and X-rays in the clinic, but also a pair of intelligent eyes that can see through cancer cells, quietly guarding our lives.

(Source: AI)