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In the pharmaceutical and consumer health industries, artificial intelligence and machine learning played an important role. These technologies are critical for the identification of patients with improved intelligence applications, such as disease detection and diagnostics for clinical testing, for medicine production and predictive forecasts. In recent years, advances in numerous analysis tools and machine learning algorithms have led to novel applications for machine learning in several areas of pharmaceutical science. This paper examines the past, present, and future impacts of machine learning on several areas, including medicine design and discovery. Artificial neural networks are employed in pharmaceutical machine learning because they can reproduce nonlinear interactions typical in pharmaceutical research. AI and learning machines are examined in everyday pharmaceutical needs, industrial and regulatory insights.

Plants are the rich source of a variety of chemicals with nutritive and therapeutic properties. The pharmacological activities of allopathic drugs are now decreased before the herbal potency. Now most of the pharmaceutical companies are focusing in this area. The Indian pharmaceutical industry is definitely looking forward to a tremendous herbal marketing. The traditionally designed neutraceutical is an Ayurvedic herbal formulation; consist of Brahmi, Tulsi, and other ingredients in powder form. This neutraceutical is proved for its antibacterial and antioxidant activity. Standardization of the traditionally designed neutraceutical is compared with the marketed product. The uses of medicinal plants are increased in developing nations. Nations like India, is a rich source of Ayurvedic plants. Government of India is also promoting the member states to formulate national policies on traditional medicine. Quality assurance is an integral part of traditional medicine. A comprehensive specification must be developed for each herbal drug preparation based on recent scientific data.DOI: http://dx.doi.org/10.3329/icpj.v1i9.11621 International Current Pharmaceutical Journal 2012, 1(9): 288-293

Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments where complex neuroimages are discriminated using ML technology. Radiologists and researchers found that sole decision by the ML algorithms is not accurate enough to implement the treatment procedure. Thus, an intelligent decision is required further by the radiologists after evaluating the ML outcomes. The current research is based on the critical ML, where radiologists’ critical thinking ability, IQ (intelligence quotient), and experience in radiology have been examined to understand how these factors affect the accuracy of neuroimaging discrimination. A primary quantitative survey has been carried out, and the data were analysed in IBM SPSS. The results showed that experience in works has a positive impact on neuroimaging discrimination accuracy. IQ and trained ML are also responsible for improving the accuracy as well. Thus, radiologists with more experience in that field are able to improve the discriminative and diagnostic capability of CML.