The impact is expected to show up in doctor-patient interactions, physicians’ paperwork load, hospital and physician practice administration, medical research, and medical education. Some compare the potential impact with the decoding of the human genome, even the rise of the internet. “We say, ‘Wow, the technology is really powerful.’ But what do we do with it to actually change things? Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. The technology also provides instant, evidence-based answers backed by guidelines, systematic reviews and peer-reviewed literature. Its scheduling capabilities are powered by artificial intelligence that matches patients with clinicians. The company’s platform has a variety of applications, including cancer research, cell therapy and developmental biology. ”What is becoming increasingly important is to develop reliable, faithful benchmarks or techniques that allow us to evaluate how well the outputs of AI models behave in the real world.” “These models provide in-silico predictions that are accurate, that scientists can then build upon and leverage in their scientific work. That impact is hidden, however, because the data isn’t obviously biased, only incomplete, even though it still contributes to a disparity in care. Celi cited research on disparities in care between English-speaking and non-English speaking patients hospitalized with diabetes. “You need to understand the data before you can build artificial intelligence,” Celi said. We need psychologists, cognitive scientists, behavioral economists, anthropologists to design human AI systems.” Furthermore, the fusion of multi-omics data strengthens predictive models, advancing personalized medicine by offering deeper insights into disease mechanisms and treatment responses. By leveraging advanced techniques such as NNs and language models, AI enables the classification of raw sequencing data with unprecedented precision. The integration of AI into NGS applications is revolutionizing the landscape of genomics, epigenomics, transcriptomics, and clinical diagnostics (Figure 2). One noteworthy application is the prediction of gene expression based on histone modification patterns, where RNNs with attention mechanisms outperformed traditional classifiers in terms of AUC (area under the curve) . ZS helps clients navigate complex challenges within industries such as medical technology, life sciences, health plans and pharmaceuticals, using advanced AI and analytics tools. Vicarious Surgical’s technology concept prompted former Microsoft chief Bill Gates to invest in the company. Using the company’s technology, surgeons can virtually shrink and explore the inside of a patient’s body in detail. These individualized programs can include digital therapeutics, care communities and coaching options. Tools like ChatGPT Health and Claude for Life Sciences can now analyze the latest medical studies and real-time biometrics to identify subtle trends — such as sleep or heart rate anomalies — weeks before symptoms appear. With these technologies, doctors can then make quicker and more accurate diagnoses, health administrators can locate electronic health records faster and patients can receive more timely and personalized treatments. The data-processing and predictive capabilities of AI enable health professionals to better manage their resources and take a more proactive approach to various aspects of healthcare. The technology comes with AI-powered features that providers can use to simplify daily tasks like scheduling. Findhelp builds technology designed to help organizations like health systems, government agencies and nonprofits identify social needs and efficiently connect people with the right care resources. Meanwhile, on the pharmaceutical side, AGI could accelerate the drug discovery process and tailor treatments to individual patients based on their unique genetic and medical profiles. Due to breakthroughs in technology, AI is speeding up this process by helping design drugs, predicting any side effects, identifying ideal candidates for clinical trials and potentially reducing costs by up to 50 percent. To give you a better understanding of the rapidly evolving field, we rounded up some examples and use cases of AI in healthcare. These approaches make AI decisions more understandable and transparent, facilitating better integration into clinical workflows. SAI emphasizes a collaborative relationship between AI systems and human users, fostering mutual understanding and trust. The implementation of XAI is one approach to improving transparency and trust, as it can help healthcare professionals better understand AI-driven decisions and incorporate them into their clinical practice (161). To address these concerns, it is essential that future AI models incorporate XAI techniques such as knowledge graphs, causal inference, and visual analytics (133, 134). 1 The digitization of health‐related data and the rapid uptake in technology are fueling transformation and progress in the development and use of AI in healthcare. All authors reviewed the manuscript. SOS and KK created figures and tables. The authors received no financial support for the research of this article. In a new Call for Papers, Communications Biology, Nature Communications and Scientific Reports are interested in submissions that highlight the possibilities offered by AI approaches to improve genomics research, regardless of the model system. Both models are trained on genomic data, The opaque models give non-traceable predictions compared to XAI, which provides predictions along with the IF–THEN rule base that is understandable to layman This figure illustrates the workflow from data input (e.g., EHRs, medical imaging) to decision support and feedback loops, highlighting the integration of AI technologies in clinical settings. This figure highlights the key stages of AI integration, from data input to decision support and model optimization, providing a comprehensive view of AI’s role in reshaping healthcare workflows. These systems synthesize real-time data from wearable devices (e.g., for monitoring heart failure or chronic obstructive pulmonary disease) with patients’ EHRs (65, 95). Moreover, AI facilitates personalized health management, offering tailored lifestyle recommendations (e.g., personalized exercise and diet plans) based on individual health data (102). Research on wearable biosensors indicates promising approaches for non-invasive biomarker detection (101). This technique is generally applied in clinical applications, especially on individuals who depend on advanced genomics and proteomics. ECG could also be used to identify patients with asymptomatic left ventricular systolic failure using convolutional neural networks. A machine-learning-based model with high accuracy and sensitivity of 80% has demonstrated promising results in predicting in-hospital duration of stay among cardiac patients . https://www.dnaxplore.com/ investigated a group of 200 patients and found that their accuracy ranged from 78.9% to 82.1 percent . Machine Learning is transforming healthcare by guiding individual and population health through a variety of computational benefits. In AI, ML is a computer-based model used to acknowledge and understand patterns in an overall volume of information to build classification and prediction models based on the training data. The attention-based approach helps identify key regulatory regions that are crucial for understanding gene regulation, providing a more refined model for genomic analysis. 32 For the remainder of this paper, we will use the term precision medicine to describe the health care philosophy and research agenda described above, and the term personalized care to reflect the impact of that philosophy on the individual receiving care. These computational methods offer avenues for more accurate, individualized treatment options, providing a significant impact on precision medicine and healthcare . DeepMind’s AlphaFold is an innovative AI technology that makes accurate predictions about protein structure, which is critical to understanding how genes work. ExPecto is another DL-based approach focused on understanding gene regulation. Understanding the genetic basis of disease is critical to developing targeted therapies, identifying individuals at higher risk, and advancing personalized treatment approaches. AI algorithms forecast off-target impacts, enabling scientists to improve the accuracy of gene editing targets. AI plays a crucial role in advancing personalized medicine by analyzing genomic data to identify individual-specific disease risks, treatment responses, and optimal therapeutic approaches. Additionally, the company’s drug re-innovation program employs AI to find new applications for existing drugs or to identify new patients. This understanding is crucial for identifying potential therapeutic targets and improving the development of personalized medicine.