Artificial intelligence - AI is a term that refers to the creation of intelligent systems that can perform tasks that would otherwise require human intelligence, e.g. learning, problem solving and decision making. Human-AI collaboration is important. Humans and AI systemswork together, collaborating to achieve common goals andusing their complementary strengths and capabilities. AI relies on digital technology. Digitization is the first step towards the application of AI. AI systems are integrated with other digital technologies: sensors, actors (cyber-physical systems (CPS) - robots), etc.In biotechnology, digital transformation can include the introduction of new technologies and processes to improve the efficiency, accuracy and speed of research and development and data analysis.Digital transformation is helping to accelerate the development and use of artificial intelligence in biotechnology by providing access to big data and automating certain tasks, which can help improve the efficiency and accuracy of research and development.
 Artificial intelligence (AI) helps solve many global problems and contributes to important sustainability development goals. Examples are food security, health and well-being, clean water, energy, responsible consumption and production, climate action, or protecting, restoring and promoting the sustainable use of all ecosystems and halting and reversing land degradation and biodiversity loss. AI is often represented in the life sciences today.AI in agriculture provides a solution to food security by adapting agricultural management to a changing climate. This includes identifying resilient crops that are more resilient to environmental changes and extremes, such as periods of drought. What makes it possible to maintain yields under abiotic stresses, which influence on crop productivity. Extreme temperatures can reduce wheat yields by 6% per C. Digitization in agriculture improves the collection and recording of data on soil health status and the future application of regenerative agricultural practices. Soil attributes and functions (soil organic matter, aggregate stability, water holding capacity, microorganism activity and nutrient availability (N, P, K)) have a positive impact on the environment and crop yield. Soil management practices affect these soil functions, because soil fertility and health are the most important resources.Assessment of soil health monitoring, using AI models, is becoming increasingly important to monitor management impacts and ensure improvements in crop productivity and sustainable agricultural systems.As awareness grows about the factors that affect human health, the concept of “health” considers the human environment and the functioning of the surrounding ecosystems as a prerequisite for healthy communities, which include the provision of fresh water, clean air, food safety and medicine.The reservoir of genetic resources for crops and soils is provided in biodiverse ecosystems that are fundamental to nutrient diversity, which is an essential factor for health, through the availability of micronutrients. There is extensive use of traditional medicine by 60% of the world’s population, and it derives from medicinal plants from wild populations and cultivation.
   AI in medicine in drug discovery and development: AI is used for identify patterns to help identify new drugs and drug targets, as well as to optimize existing therapies. AI in personalized medicine: AI is used for an individual’s genomic data and other types of health data to develop personalized treatment plans that are tailored to their specific needs: using machine learning algorithms to predict an individual’s response to a particular treatment and to identify potential adverse reactions. AI in disease diagnosis and prediction: AI is used to analyze data e.g. electronic health records and wearable devices, to identify patterns and correlations that may indicate the presence of a particular disease,and it helpsto improve the accuracy of diagnoses and enable earlier interventions to prevent the progression of the disease. AI in Biomedical Image Analysis: AI is used for medical images, e.g. CT scan and MRI images, to identify abnormalities and diagnose diseases, using deep learning algorithms for automatic segmentation and classification of structures in medical images. The well-documented advancements in data-driven complex machine learning techniques that arose within the wave of artificial intelligence (AI) prompted the exploration of possible AI applications in various fields and aspects of human life, habits, and society. By providing autonomous feature engineering that outperforms traditional methods that rely on manual feature engineering and achieves performance that is on par with or even greater than that of humans in some domains, deep learning models significantly reduce the need for domain-expert knowledge. To completely trust, embrace, and integrate rapidly developing AI solutions into our daily lives and practices, we need human-centric explainable AI (HC-XAI) that can provide human-understandable interpretations for their algorithmic behaviour and consequences.
  This will enable us to keep control over and make constant improvements to the explainability, accountability, performance, and fairness of AI systems during their entire existence. Following this inspiration, the lately emerging trend in various and multidisciplinary research groups is based on the exploration of human-centric AI techniques and the development of contextual explanatory models. The next wave of artificial intelligence (AI) is being built on the synergy between AI and HI, which is being fuelled by these initiatives. Healthcare AI integration has revolutionary potential, but in order to fully realise this potential, major obstacles must be removed. This study emphasises the need for long-term research to fully evaluate AI efficacy over time, particularly when it comes to treating comorbid illnesses when treatment complexity rises. To encourage responsible AI use, ethical concerns like data privacy, algorithmic bias, and accountability must be addressed immediately. Furthermore, improving healthcare workers training on AI technology and incorporating patients in the creation of AI tools are crucial stages for successful deployment. The healthcare industry may use AI to greatly enhance diagnostic capabilities and treatment outcomes by encouraging interdisciplinary collaboration, creating thorough regulatory frameworks,