In the last few years, an increasing interest has developed regarding the application of different techniques for the identification of pollutants inside the tissues deriving from patients affected by benign or neoplastic diseases. Particular attention was paid to neoplasia linked to particular exposures, e.g., heavy metals, carbon dusts, silica, asbestos. As regards the last pollutant, a wide body of scientific literature has been collected, considering the severe effects caused by mineral fibers on human health. Optical and electronic microscopies were widely applied to identify the fibers in respiratory and extra-respiratory organs to detect the minerals and to link their presence to an exposure source and to understand their role in cancer development. The main advantage of electron microscopy lies in the possibility of coupling the microscopes with energy dispersive spectrometers and also collecting data on the elemental composition of various inorganic phases. In term of sample preparation and time of analysis, the most utilized microscope technique is Scanning Electron Microscopy with an annexed energy dispersive spectrometer (SEM/EDS), allowing for the morphological and chemical characterization of the observed particles/fibers. Moreover, this technique is envisaged by Italian Law for asbestos identification in air and bulk samples. On the other hand, this technique does not allow a reliable identification of the mineral phase in the case of polymorphs with the same chemical formula but different crystal structures. In this work, the coupling of a spectroscopical technique—micro-Raman spectroscopy—to SEM/EDS is proposed for a sure phase identification of particles, showing EDS spectra with ambiguous phase identification, observed in samples of tissues from patients affected by colorectal cancer and living in an asbestos-polluted area. In these tissues, different particles with EDS spectra that do not allow a sure identification of the phase—in particular calcium-rich particles and titanium oxides—were successively analyzed by micro-Raman spectroscopy. Thanks to this last technique, it was possible to ascribe the mineral phases associated to these particles to “aragonite” (a calcium carbonate polymorph) and to “anatase” (a Ti dioxide polymorph).

Asbestos, consisting of six natural mineral fibrous silicate phases, was widely utilized in industrial development during the 20th century and has left a global legacy of health, environmental, and regulatory challenges. Its remarkable properties (e.g., heat resistance, sound absorption, and tensile strength) made it a useful material in numerous applications. However, scientific research revealed its serious health risks in the early 1900s, with growing evidence during the 1960s, and nowadays its role in the development of different diseases (e.g., respiratory diseases, such as lung cancer, mesothelioma, and asbestosis) is well defined. Mapping this complex history requires integrating heterogeneous and often inconsistent information from nearly 200 countries. In this study, we tested the use of generative artificial intelligence (AI) tools as exploratory and comparative instruments to support the collection of asbestos-related data worldwide. Using Google Gemini (version 2.5 flash) and OpenAI ChatGPT (GPT-4-turbo variant), we gathered historical, medical, and regulatory information and then systematically verified and contextualized it with expert analysis. This dual approach allowed us to assess both the global asbestos situation and the reliability, advantages, and limitations of AI-assisted research. Our results highlight how AI can accelerate data collection and provide useful first drafts while underscoring the necessity of human expertise for validation, interpretation, and critical integration. This study, therefore, contributes a dual perspective: a comprehensive overview of the asbestos legacy across countries and a methodological reflection on the opportunities and pitfalls of employing AI in geoscientific and environmental research.