Total recall of oncology knowhow is a request over medical precision in cancer prognostics and diagnostics, which are very important basis activities, prior to subsequent radical Medicare and Medicaid, prolonging survival chances for more than 10 years, significantly. Why can one patient reverse their autoimmune state, and another not? Is there a key protein or cellular system with a ‘half-time’ of efficacy and survival that defines this window? Another sincere question remained: how can we reframe medical precision in a mechanistic layer now? Recent global datasets reveal persistent, accelerated, and excessive mortality POSTCOVID-19 viral attacks [1,2,4], with monthly death rates exceeding pre-pandemic baselines by 20–40% in several regions. These trends coincide with rising (CO-)morbidities among an estimated 400–600 million long-COVID patients, increasing treatment-related injuries, and stagnation in long-term cancer survival despite three decades of technological innovation [1–3]. The central question remains unresolved, which this commentary focused on is: why can one cancer patient survive more than 10 years, and reverse autoimmune or malignant progression, and another not, randomly?

Recently reported accelerated and excess mortality rates are warning signals that cannot be ignored over the last years [3–5]. Over the past three decades, Cardiology and Oncologic Developments (CODs) have undergone a dramatic technological shift(s), while some oncologic routines and developments have not, remarkably [1,2].

On one hand, still nobody knows how the Survival Chance of Cancer Patients (SCCP) could be prolonged for more than 10- even 20 years, significantly. The bias-based differences between legacy prognostics-diagnostics, and subsequently random Medicare and Medicaid's progressions are undeniable. On the other hand, many miscommunications and rigid legacy guidelines are causing unknown collateral damage and unpredictable side effects i.e. A) Treatments have moved recently from (non-)specific cytotoxic chemotherapy [1,6] toward so-called targeted therapies, immunotherapies with unpredicted outcomes, and even so-called cancer vaccines [7], which could not again prolong SCCP-10/20-year survival chance, eventually. Furthermore, B) Radiotherapy has evolved from broad, crude fields to IMRT, VMAT, proton therapy, and image-guided systems marketed as “precision tools” [6], which also did not affect SCCP-10/20 years, however. Moreover, these recently known and unknown tools are often presented as transformative breakthroughs, which are also using the so-called AI tools and software appropriately; remaining failing the patient's goal. Yet clinical reality remains far more complex in this POSTCOVID-19 era, including 400 up to 600 million long COVID-patients [2–4].

For children, for example, the most meaningful goal (fair expectation) is that survival gains were achieved when they can survive cancerogenic processes for at least 40–50 years, while for adults, at least an expected survival chance for more than 20 years. The persistence of preventable mortality—especially among children with biologically curable diseases—underscores systemic failures i.e. random late detections, rigid algorithms, selective high-cost therapies with marginal benefit, and subsidized research pipelines that do not yield proportional survival gains. As global cancer mortality continues to rise, and mortality-to-incidence ratios remain high even in high-income countries, a transparent, mechanistic reframing of medical precision is urgently required. This commentary argues that the next era of diagnostics and prognostics must innovate/ integrate mechanistic biology, real-world toxicity, long-term outcomes, and unbiased epidemiological data. Only through such recalibration can precision medicine evolve from a marketing narrative into a scientifically grounded, patient-relevant framework.

In the 1990's, long before the current wave of AI-driven diagnostics and molecular therapeutics took over fact-based sciences, instead of hypothetical biotechnological /mathematical speculations, cancer-minimum survival time-expectancy after diagnosis & treatments was set at 5 years and now is the same, no progression at all with(out) AI-toys, significantly. Moreover, by contrast, the last 6 years—marked by the POST-COVID-19 pandemic attack—have coincided with accelerated and excessive mortality across multiple cancer patient groups, including those with lung cancer, cardiovascular disease (CVDs), and multimorbidity, so-called uncurable diseases. Different questions still have no straightforward answers. These questions raise legitimate questions about how much of the promised “Precision Medicine approaches” have translated into real-world benefits, eventually. While modern tools can improve tumor control/monitoring and reduce certain acute toxicities, the narrative of near-perfect targeting remains scientifically inaccurate, paradoxically.

Many pharmaceutical agencies claims over “100% targeted” or “highly precise” agents are obscure/not validated; yet the facts remain: 1. Targeted drugs still affect pathways present in normal tissues, producing severe off-target toxicities. 2. Immunotherapies can induce systemic, chronic, and sometimes irreversible immune-related adverse events, despite being promoted as the pinnacle of precision. 3. Radiotherapy—regardless of delivery platform—inevitably exposes surrounding normal tissue, causing fibrosis, organ dysfunction, and secondary malignancies. On the other hand, 4. Why the so-called “Precision Medicine” in oncology and cardiology cannot still be defined as neither exact nor a relative concept, has certain reasons, which cannot be compared with the limited progressions of counterparts. Besides, it is not a guarantee of minimal harm, and significant acute and/or chronic side effects, i.e., different kinds of TRALIs, ALIs, chronic GVHDs, etc. One of the essential undefined concepts is the accelerated excessive mortality rates, which has always been a significant warning signal that cannot be ignored in the last 5 years.

Different comparison studies and statistical data published by the WHO and the Our World in Data (3) show that between 2020 and 2024, many countries experienced persistent, accelerated, and excessive mortality rates, compared to the last decades, including weekly and monthly death rates rising well above the 2015–2019 baseline. According to data statistics published by the www.WHO.int and www.ourworldindata.org in these POSTCOVID-19 era, with rising excess morbidity rates of 400 million long COVID patients (+7 million mortality rates), and increasing recognition of chronic treatment-related injuries, it is essential to re-evaluate legacy diagnostics, outdated prognostic models, and routine treatment protocols, which are (ab)using chemical drugs as the main treatments, that may no longer reflect current biological, epidemiological, or societal realities.

Now (2026), there are enough tools and evidence-based data published that need to be reconsidered as an update/upgrade of old-fashioned and bias-based routine guidelines. A science-based, transparent reassessment is urgently needed—one that prioritizes long-term outcomes, real-world toxicity, and honest communication over marketing narratives.

Based on recent publications, in some periods, excess mortality exceeded 20–40% in certain regions, reflecting a combination of randomly delayed care, disrupted diagnostics, and strained health systems. Simultaneously, the WHO’s Global Cancer Observatory reported that cancer mortality continues to rise globally, with updated www.WHO.int datasets showing that an increasing mortality trends across multiple cancer types, even in high-income countries with advanced healthcare systems, POSTCOVID-19 pandemic eras. Different analysis published online further confirms that the mortality-to-incidence ratios remain high in many countries, indicating that legacy diagnostics, early detection and subsequently, an effective treatment are still failing, worldwide. These reports are not abstract numbers and fake news presented offline and online, they are warning signs, over current bias-based Pro- and Diagnostics activities, including their subsequent cures, which should be revised/ updated.

One sincere expectation is that when the abovementioned bias-based activities could be revised, the updated modern guidelines might affect (in)directly preventable deaths—especially many among children and young adults. Cancer and cancerogenic processes, as previously described in my invented DTM model systems, since the 2018-prepandemic periods, (1) remained a leading model system a mechanistic predictor of cause-effects, especially of mysterious cause of excess mortalities and morbidities globally.

Simultaneously there are largely attributable risk factors, which are modifiable/preventable risks for example, 30 different independent factors such as preventing tobacco smoking, decreasing alcohol consumption, high body mass index, promoting sufficient physical activity and sport, creating smokeless areas, promoting optimal breastfeeding, decreasing air pollution, protecting against harmful radiation/ infectious viral antigens, immunogens, allergens (AIAs) etc. that also could be managed.

Besides, different official institutions like Dutch Heart Foundation, and recently global epidemiological study data also compared the last decades statistics (pre-pandemic between 1990-2015) over the Global Burden of Diseases, injuries, excessive mortality rates, and different modifiable risk factor etc. and provided robust evidence-based data defining certain new risk factors, which have mutated aggressive progression causing certain (un)curable diseases (cancerogenic) and their processes, as previously defined and predicted [1,2].

By providing national and subnational assessments spanning the past 25 years, these studies can inform debates on the importance of addressing risks in context and highlight more over fake claims over modern alternatives to prolong SCCP10/20 years, however. It is indeed noteworthy that many global exposures decreased in the last decades, viz. Sanitation-associated infections, childhood diseases, and percentage of smoking (areas); which each exposure independently, could be considered as the main cause/inducers/initiators of different cancerogenic processes. All abovementioned modalities and risks' management have been separated and/or jointly (re)evaluated in the last decade, indicating that an increase in any kind of AIAs propagation can contribute to notable increases in cancerogenic risk factors worldwide, unequivocally [1–7].

Another sincere mind provoking aspect is that these kinds of preventable excessive mortality rates have become an unacceptable reality: Children still are dying from curable diseases now. Despite a Century of research, 20–40% of children with cancer or severe infectious diseases could not survive all routine oncologic routine treatments. This is especially troubling given that many pediatric cancers are biologically curable! When they have detected early and treated appropriately. The persistence of these mortality rates in the post-COVID-19 era—when data, tools, and computational models are more advanced than ever—demands a critical assessment of our medical paradigms.

The problem is not a lack of knowledge, remarkably. It is the continued reliance on legacy different missing links, and failures for example: 1. Legacy diagnostics that detect disease too late, 2. rigid, dogmatic algorithms that ignore Mechanistic biology, 3. bias-based treatment pathways shaped by tradition rather than evidence, 4. selective, expensive therapies that offer marginal benefit yet impose severe toxicity and last but not least 5. subsidized research pipelines that consume public funds without delivering proportional improvements in survival, especially in this POSTCOVID-19 period.

The take-home message is that taxpayers, cancer patients and their families are not only paying financially, but also, they are paying with their lives, but SCCP-10/20 years did not change significantly, after 100 years R&Ds. Conclusively, could be said that if and when the main Policymakers, Medical Scientists, and Pharmaceutical Companies failed to prolong SSCPs 10–20 years and it is highest time to break these bias-based managerial cycles, the medical and scientific community must address several foundational questions that have been neglected for decades, soon or later.