Artificial intelligence (AI) has become increasingly central to precision medicine by enabling the integration and interpretation of multimodal data, yet implementation in clinical settings remains limited. This paper provides a scoping review of literature from 2019-2024 on the implementation of AI in precision medicine, identifying key barriers and enablers across data quality, clinical reliability, workflow integration, and governance. Through an ecosystem-based framework, we highlight the interdependent relationships shaping real-world translation and propose future directions to support trustworthy and sustainable implementation. Traditional healthcare models have difficulty addressing the complexity of modern healthcare needs, particularly given the increasingly multimodal nature of health data spanning genetic, clinical, behavioral, environmental, and lifestyle information (Topol, 2023; Judge et al., 2024; Schouten et al., 2025). As precision medicine emerges as a promising solution for integrating multimodal data into healthcare, a new implementation strategy is necessary due to the complexity of existing healthcare structures and the extent of interdisciplinary collaboration that is now required (Tobias et al., 2023).

The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibliography, and applying advanced analytics methods to discover useful patterns. The goal is to turn large amounts of available data into actionable information to authorities for planning public health activities and policies. The integration and analysis of these heterogeneous sources of information will enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. The project offers a common representation schema for the heterogeneous data sources. The iASiS infrastructure is able to convert clinical notes into usable data, combine them with genomic data, related bibliography, image data and more, and create a global knowledge base. This facilitates the use of intelligent methods in order to discover useful patterns across different resources. Using semantic integration of data gives the opportunity to generate information that is rich, auditable and reliable. This information can be used to provide better care, reduce errors and create more confidence in sharing data, thus providing more insights and opportunities. Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer.

Artificial intelligence had its break out year in 2023. We chose those two language models because they use live information from the internet to make their predictions, unlike ChatGPT and Microsoft's Bing which rely on older data. AGI is a theoretical intelligent agent able to complete any intellectual task a human can - and the arrival of AGI is forecast to cause huge changes to human society. Backed by Google and Amazon, Claude's parent company Anthropic was founded by former members of OpenAI, makers of ChatGPT. 'In recent years we've seen AI algorithms match or exceed human performance in specialized tasks like object recognition, game playing, and language processing.

This paper provides an overview of the current and near-future applications of Artificial Intelligence (AI) in Medicine and Health Care and presents a classification according to their ethical and societal aspects, potential benefits and pitfalls, and issues that can be considered controversial and are not deeply discussed in the literature. This work is based on an analysis of the state of the art of research and technology, including existing software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. Motivated by our review, we present and describe the notion of 'extended personalized medicine', we then review existing applications of AI in medicine and healthcare and explore the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks that even question fundamental medical concepts. Many of these topics coincide with urgent priorities recently defined by the World Health Organization for the coming decade. In addition, we study the transformations of the roles of doctors and patients in an age of ubiquitous information, identify the risk of a division of Medicine into 'fake-based', 'patient-generated', and 'scientifically tailored', and draw the attention of some aspects that need further thorough analysis and public debate.

In the very near future, you may visit your doctor and receive a drug therapy that was customized just for you based on your DNA, metabolism, and lifestyle. Advances in key technologies, including decoding the human genome (DNA), artificial intelligence (AI), and health information, are the catalysts for the rapidly accelerating field of personalized medicine. Navid Alipour, JD (Moderator) Co-founder and Managing Partner, Analytics Ventures Navid Alipour is Co-founder and Managing Partner at Analytics Ventures, a Venture Formation Fund focused on starting new ventures with artificial intelligence and machine learning at their core. Prior to co-founding Analytics Ventures, he founded La Costa Investment Group, making investments in startups nationally. Through the founding of multiple Artificial Intelligence(AI) based companies like CureMetrix and CureMatch, Navid is a long-time entrepreneur in the AI space, and looks to address the need between angel investors and big venture capital funds.

A Chinese genetic scientist recently caused an ethical outcry by claiming to have created the world's first gene-edited babies, using CRISPR to modify their DNA before birth to make them resistant to HIV. The procedure, which is effectively barred in most countries, highlights the far-reaching potential of gene editing and CRISPR as well as the ethical, moral and social implications of going too far. And quite understandably, the incident has reignited the conversation around AI and how gene editing should be regulated. CRISPR is a gene editing technology that can be used to precision target specific DNA sequences to deliberately activate or inhibit genes. Ever since it was introduced six years ago, much of the focus has been on CRISPR's potential to treat genetic diseases and its impact on drug discovery and development. The ability to target an individual's distinctive molecular and genetic profile opens up new opportunities in precision or personalized medicine.

Recordings of electrical brain activity carry information about a person's cognitive health. For recording EEG signals, a very common setting is for a subject to be at rest with its eyes closed. Analysis of these recordings often involve a dimensionality reduction step in which electrodes are grouped into 10 or more regions (depending on the number of electrodes available). Then an average over each group is taken which serves as a feature in subsequent evaluation. Currently, the most prominent features used in clinical practice are based on spectral power densities. In our work we consider a simplified grouping of electrodes into two regions only. In addition to spectral features we introduce a secondary, non-redundant view on brain activity through the lens of Tsallis Entropy $S_{q=2}$. We further take EEG measurements not only in an eyes closed (ec) but also in an eyes open (eo) state. For our cohort of healthy controls (HC) and individuals suffering from Parkinson's disease (PD), the question we are asking is the following: How well can one discriminate between HC and PD within this simplified, binary grouping? This question is motivated by the commercial availability of inexpensive and easy to use portable EEG devices. If enough information is retained in this binary grouping, then such simple devices could potentially be used as personal monitoring tools, as standard screening tools by general practitioners or as digital biomarkers for easy long term monitoring during neurological studies.

The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions.

The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions.

Electronic health records (EHRs) are an emerging relational domain with large potential to improve clinical outcomes. We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational functional gradient boosting, outperforms propositional learners particularly in the medically-relevant high recall region. We observe that both SRL algorithms predict outcomes better than their propositional analogs and suggest how our methods can augment current epidemiological practices.