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Another interesting application of DL in drug discovery is the generation of new chemical structures through neural networks (Fig. 2.2). This also includes protein engineering involving the molecular design of proteins with specific binding or functions. Drug discovery and development is an immensely long, costly, and complex process that can often take more than 10 years from identification of molecular targets until a drug product is approved and marketed. Any failure during this process has a large financial impact, and in fact most drug candidates fail sometime during development and never make it onto the market. On top of that are the ever-increasing regulatory obstacles and the difficulties in continuously discovering drug molecules that are substantially better than what is currently marketed. This makes the drug innovation process both challenging and inefficient with a high price tag on any new drug products that make it onto the market 14. Mammograms serve as a key screening tool but limited radiologist time and expertise constrain oversight. There are large volume of study and review on AI in healthcare highlighted the dominance of imaging-based specialties such as radiology, gastroenterology, and ophthalmology in AI research. However, AI technologies – including large language models – are being rapidly deployed, sometimes without a full understanding of how they may perform, which could either benefit or harm end-users, including health-care professionals and patients. Furthermore, the development of models that can help clinicians detect postoperative complications such as infections will contribute toward a more efficient system 65. Peer review This paper also addresses the current limitations to the use of AI in clinical practice and explores possible solutions. Furthermore, this review considers potential future applications and strategies for more streamlined implementation into wider healthcare systems. We conducted a structured literature search in Ovid MEDLINE (2018–2025) using terms related to AI, machine learning, deep learning, large language models, generative AI, and healthcare applications. Priority was given to peer-reviewed articles providing novel insights, multidisciplinary perspectives, and coverage of underrepresented domains. How AI is Reshaping Decision-Making Using the stored data, the framework of DeepCare can model disease progression, support intervention recommendation, and provide disease prognosis based on EMR databases. Studying data from a cohort of diabetic and mental health patients it was demonstrated that DeepCare could predict the progression of disease, optimal interventions, and assessing the likelihood for readmission 37. The implementation of AI in healthcare decision-making processes raises a range of ethical questions. A conscientious examination of the transparency and accountability of AI algorithms, potential biases inherent in data and algorithms and the attribution of liability for AI-generated decisions is imperative. Furthermore, the process of integrating AI into pre-existing healthcare systems and workflows can be challenging. Views on patient use of AI For instance, AI algorithms can analyze medical images, such as X-rays and MRIs, with greater accuracy and speed than human radiologists, often detecting diseases such as cancer at earlier stages. Natural language processing (NLP) is a form of artificial intelligence that enables computers to interpret and understand human language. In healthcare, NLP is reshaping the industry by allowing technology to extract meaningful insights from vast amounts of clinical data and power more intelligent, automated workflows—capabilities that now sit at the core of many leading risk adjustment solutions for 2026. AI has the potential to revolutionize clinical practice, but several challenges must be addressed to realize its full potential. Data privacy, availability, and security are also potential limitations to applying AI in clinical practice. Additionally, determining relevant clinical metrics and selecting an appropriate methodology is crucial to achieving the desired outcomes. Human contribution to the design and application of AI tools is subject to bias and could be amplified by AI if not closely monitored 113. Natural Language Processing PathAI worked with drug developers like Bristol-Myers Squibb and organizations like the Bill & Melinda Gates Foundation to expand its AI technology into other healthcare industries. Qventus is an AI-based software platform that solves operational challenges, including those related to emergency rooms and patient safety. The company’s automated platform can prioritize patient illness and injury and tracks hospital waiting times to help hospitals and health systems optimize care delivery. Valo uses artificial intelligence to achieve its mission of transforming the drug discovery and development process. Availability of data and materials However, this improvement in performance results in it being more expensive to implement GPT-4 compared to GPT-3 168. Therefore, it will also be important to ensure that when AI is used in underserved communities, that these communities are not relegated to cheaper, less effective forms of AI that can further perpetuate health disparities. Furthermore, it is crucial to consider that societies and individualistic cultures rapidly evolve, underscoring the need for AI programs to be updated with the help of experts in those particular societies to convey information in line with their evolving values. This advanced analysis reduces the likelihood of missed appointments or the need for rescheduling, leading to better outcomes and increased efficiency in the healthcare system. For example, AI can identify patterns in patient behavior and appointment history that may not be evident in traditional scheduling systems, optimizing the scheduling process to better meet patient needs and provider availability. Virtual assistant chatbots can provide personalized medical support and education to patients based on their individual needs and preferences. By utilizing Natural Language Processing (NLP) and machine learning algorithms, chatbots can learn from patient interactions and adjust their responses to match the patient’s language and style, making the user experience more natural and engaging5,6. Moreover, virtual assistant chatbots can offer round-the-clock service, which is particularly valuable for patients who are unable to access healthcare providers during regular working hours. With 24/7 availability, chatbots can help patients obtain the information and support they need when they need it. Many experts contend that the mastery of AI is a skill that necessitates nurturing, akin to other competencies acquired during medical school 156,157. People’s feelings about AI replacing or augmenting human healthcare practitioners, its role in educating and empowering patients, and