Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through ML. DL is a larger family of ML methods based on symbolic learning and simulated neural networks.

Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice.

 Researchers Vibhas Chugh, Adreeja Basu, Ajeet Kaushik, Manshu, Shekhar Bhansalid, and Aviru Kumar Basu cover contemporary methods used in modelling cancer development in a study titled ‘Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer’.

According to WHO (World Health Organization) data from 2018, cancer was the only factor in 9.6 million deaths, making it the most prevalent cause of death.1 On a global scale, cancer is said to be the sixth leading cause of mortality. This highlights the critical need to develop fresher, more targeted treatment plans for cancer. Cancer is a broad concept; it describes the sickness that develops after biological alterations that lead to unchecked cell proliferation and division.

AI and health wellness

AI-powered forecasting algorithms are now a vital part of cancer treatment. By recognizing the risk variables, predictive models can determine a person’s likelihood of developing a specific cancer. AI can identify people who are at a greater risk of catching the disease ahead of it spreading. This makes it possible for medical experts to closely monitor these patients and take prompt action as and when required. 

Intelligent tools for early disease prediction, effective screening, and ongoing monitoring are made possible by the application of AI, ML, and DL approaches. These techniques make use of data patterns to predict possible health hazards, expedite the diagnostic procedure, and offer patient monitoring in real time. The combination of AI, ML, and DL provides proactive insights for healthcare professionals, leading to a paradigm shift in the field towards personalized and preventive medicine and ultimately improving the overall effectiveness of illness management. 

The proposed study identifies the use of AI to predict different types of cancer. It examines the role of AI in predicting colorectal, Breast, lung, and pancreatic cancer. It also identifies the content of using AI-based ML/DL methods, such as ethical issues, governance, algorithmic impartiality, data bias, and safety. Significant continuing efforts regarding medical AI are focused on creating ethical guidelines and norms.

Next generation materials

A wide range of sensor applications in medicine, wearable electronics, security, the environment, defence, and agriculture have been transformed by integrating 2D nanomaterials with IoTs, AI, and ML. The development of graphene, borophene, and MXene as advanced 2D materials (A2M) for constructing next-generation sensors is due to their distinctive physicochemical properties and surface functions. Their unique electronic, mechanical, and optical properties provide an attractive platform for biosensing applications. However, harnessing their full potential for effective screening of cancer biomarkers presents challenges such as sensitivity and specificity in biomarker detection, stability in biological environments, economic and scalability concerns.

The medical field is experiencing a seismic shift with the integration of AI, and nowhere is this more palpable than in cancer imaging. Most radiologists have recognized the transformative potential of AI-driven therapeutic applications, making it an exciting frontier for innovation and research. Cancer imaging is undergoing rapid evolution. Advancements in AI, especially those rooted in ML, are paving the way for more accurate, efficient, and timely diagnostic procedures.

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