Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

Habiba Abdelhalim, Asude Berber, Mudassir Lodi, Rihi Jain, Achuth Nair, Anirudh Pappu, Kush Patel, Vignesh Venkat, Cynthia Venkatesan, Raghu Wable, Matthew Dinatale, Allyson Fu, Vikram Iyer, Ishan Kalove, Marc Kleyman, Joseph Koutsoutis, David Menna, Mayank Paliwal, Nishi Patel, Thirth Patel, Zara Rafique, Rothela Samadi, Roshan Varadhan, Shreyas Bolla, Sreya Vadapalli and Zeeshan Ahmed

Published Year: 07/06/2022


Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.


Precision medicine is the utilization of healthcare tools to create specialized treatments that consist of optimal actions for the patient, based on the data available (König et al., 2017Pinho, 2017Gameiro et al., 2018Ginsburg and Phillips, 2018Ahmed et al., 2020aAhmed, 2020Elemento, 2020Faulkner et al., 2020Ahmed et al., 2021a). As clinical, genomic, and metabolic data become easier to obtain and interpret in relation to complex and chronic diseases such as cancer, disease treatment will become more effective (McAlister et al., 2017Pinho, 2017Ginsburg and Phillips, 2018Goetz and Schork, 2018Bilkey et al., 2019Ahmed et al., 2020aAhmed, 2020Faulkner et al., 2020Ahmed et al., 2021a). In the current state of healthcare, healthcare professionals tend to divide their attention and plan treatments based on symptoms (McAlister et al., 2017Bilkey et al., 2019). However, symptoms like pain, vary from patient-to-patient and may even be absent in life-threatening situations (Lazaridis et al., 2014McAlister et al., 2017Pinho, 2017Goetz and Schork, 2018Bilkey et al., 2019). Since symptoms can greatly vary between patients, utilizing genomic and metabolic data in conjunction with clinical data from previous patients enables clinicians can prescribe a better, more personalized treatment plan (McAlister et al., 2017Goetz and Schork, 2018). Thus, the development and implementation of precision medicine should improve the quality of healthcare compared to the conventional system dominated by symptom-driven medicine (Lazaridis et al., 2014McAlister et al., 2017Pinho, 2017Ginsburg and Phillips, 2018Goetz and Schork, 2018Bilkey et al., 2019Ahmed et al., 2020aFaulkner et al., 2020Ahmed et al., 2021a).

International interest in precision medicine could be seen as early as 2011, with the American Association for Cancer Research’s (AARC) Project GENIE, which utilized several “big data initiatives” such as Genomics Evidence Neoplasia Information Exchange (GENIE) (Sweeney et al., 2017). The aim of this project was to address the challenges that came with sharing large amounts of genomics and clinical data, specifically regarding effective cancer therapies (Micheel et al., 2018). This level of innovation in precision medicine coincided with a decline in costs of DNA-sequencing techniques and a more widespread adoption of electronic medical records, which conveniently allowed for sharing and analysis of data (Bentley, 2006Shendure et al., 2008Evans, 2016Kruse et al., 2016Garrido-Cardenas et al., 2017Graber et al., 2017Howe et al., 2018). One common application of precision medicine in the United States is genetic screening, which is used to predict and diagnose critical conditions, which can reduce rates of morbidity (Smed et al., 2021). Another emerging application is the prescription of drugs based on genetic markers of efficacy. For instance, studies have shown that seizure drug carbamazepine having the HLA-B*1502 gene is highly likely to experience adverse effects. However, the integration of these genetic markers in prescribing drugs requires strong evidence of clinical validity first. However, the integration of these genetic markers in the prescription of drugs first require strong evidence of clinical validity at all stages of the drug product cycle (Sweeney et al., 2017Ginsburg and Phillips, 2018). Despite pharmacogenomics being in the early stages of development, it shows great promise toward driving patient-specific outcomes.