AI is a tool that facilitates research. On its own cannot accomplished anything meaningful therapeutically, but it can help to select best direction and formulate a hypothesis. Once the hypothesis is formulated it needs to be proven or disproven using hard work and animal studies before submission of clinical protocol of clinical trial in human suffering from cancer can be even submitted for consideration to the Regulatory Agencies (Health Canada, FDA or European Regulatory Agency.

The combination of artificial intelligence (AI) and medicine holds great promise for improving cancer diagnosis, treatment, and overall patient management. Still, it is important to understand the current capabilities and limitations.

1. Enhanced Diagnosis and Early Detection: AI-driven algorithms can analyze imaging data (e.g., MRI, CT scans, PET scans) more rapidly and with a level of detail that rivals—or in some cases, even surpasses—human experts. Machine learning models can identify subtle patterns and biomarkers that may be early indicators of cancer, enabling earlier intervention. In addition, AI can assist in the interpretation of genetic tests and molecular profiles, guiding the selection of targeted therapies suited to a patient’s unique tumor characteristics.

2. Drug Discovery and Personalized Treatment: AI can speed up drug discovery by sifting through vast amounts of biomedical and pharmacological data. This computational approach helps identify promising compounds more efficiently, predict their potential efficacy, and minimize costly trial-and-error in the laboratory. Moreover, AI-driven analyses of patient data—including genetic makeup, lifestyle factors, and treatment history—can help physicians tailor treatments to the individual, optimizing dosage and drug combinations. This personalization can improve outcomes and reduce side effects, particularly in aggressive cancers or conditions like leukemia.

3. Predictive Analytics and Prognosis: Advanced machine learning models can analyze a patient’s clinical history and other risk factors to forecast the disease’s progression, likely responses to certain treatments, and possible adverse effects. Such insights support oncologists in making evidence-based decisions, ultimately improving long-term survival and quality of life for patients.

4. Limitations and Ongoing Challenges: Despite these promising developments, AI is not yet a panacea. Cancers are highly complex, diverse diseases with numerous molecular subtypes and resistance mechanisms. While AI can assist in drug discovery and optimize treatment strategies, it cannot simply “cure” cancer on its own. It is a tool that enhances and augments human expertise, rather than replacing it. Translating AI-driven insights into widely accessible, standard-of-care treatments remains a major challenge. Regulatory hurdles, the need for extensive clinical validation, and the complexity of integrating AI solutions into existing healthcare infrastructures must still be overcome.

In Conclusion: AI, when combined with modern medicine, greatly improves our ability to detect, characterize, and treat cancer and other severe conditions. Although it has not yet unlocked a universal cure, it significantly accelerates research progress and personalizes treatment approaches. Over time, as algorithms become more sophisticated and data integration improves, AI may help bring us closer to more effective and potentially curative therapies.

(This statement represents my personal opinion only)...