Rewiring Medicine: AI’s Data-Driven Leap Toward Personalized Healthcare

 

Mr. Jagdish Kumar Vinjamuri

 

Principal Product Manager (Tech), Aetna Inc. (A CVS Health Company)

vinjagdish@gmail.com

 

ABSTRACT

AI is playing an important role in medicine by relying on large, varied health data to shape personalized healthcare. The article talks about how advanced AI helps move away from standard health plans to support better treatment options shaped for each person’s specific genetic, medical and life style traits. AI can give accurate diagnoses, predict what stage a medical condition will be in and help with treatment options by using electronic health records, genomics, devices attached to the body and continuous monitoring, improving patient results and lowering healthcare spending. There is also a focus on the ethics and practical problems that come with this transformation such as keeping data private, avoiding biased algorithms and using AI that people can see how it works. In essence, the article outlines how AI and data in medicine will help clinicians and patients, leading to a healthcare system that responds better, is more efficient and fairer.

KEYWORDS

Artificial Intelligence, Personalized Medicine, Data-Driven Healthcare, Predictive Analytics, Precision Diagnostics, Clinical Decision Support, Machine Learning Models, Healthcare Informatics, Patient-Centric Care.

How to Cite: Mr. Jagdish Kumar Vinjamuri, (2024) Rewiring Medicine: AI’s Data-Driven Leap Toward Personalized Healthcare, European Journal of Clinical Pharmacy, Vol.6, No.1, pp. 34-42

INTRODUCTION

For many years, medicine treated patients using general clinical guidelines and statistics rather than considering what was unique to each person. Despite many successes, this traditional approach has hit several obstacles when clinicians work with patients whose responses, how their diseases progress and genetic factors are very different. Standardizing care in ways that limit variation can cause problems such as inaccuracies, inefficiency, unwanted side effects and growing health care costs (Hamburg & Collins, 2010). Realizing these problems has made more people seek healthcare that meets the unique needs of each patient.

 

Because of these challenges, healthcare is moving toward a system that relies on data, especially health information from EHRs, genomic studies and devices used for tracking health. By adopting AI, healthcare is being reinvented as data is used to identify patterns and useful information for more accurate diagnoses and target treatments. Big data and modern computing tools are making it possible for innovations that can both boost treatment quality and help worldwide healthcare systems run more smoothly (Topol, 2019).

 

One of the main reasons for this revolution is artificial intelligence (AI) which powers the personalization of medicine. Thanks to machine learning, natural language processing and computer vision, AI is capable of analyzing a lot of data and recognizing hidden details that regular clinical staff might not observe (Jiang et al., 2017). Because of AI, healthcare now has the ability to identify disease risks in advance, monitor people’s health over time and take actions to keep problems from developing (Rajkomar et al., 2019). When clinicians have useful data insights, AI supports a more reactive and agile way of caring for patients.

 

This article looks at how AI is helping to change healthcare into a personalized service for patients. It will look into the ways AI personalizes healthcare services, show major uses in diagnostics, treatments and monitoring and explore the various aspects for patients, doctors and medical institutions. Exploring these ideas, the article aims to give readers a clear picture of the positives and negatives of using AI in medicine, picturing a future where medical care is perfectly fitted to each person’s biology, surroundings and habits.

 

Table I:

 

FOUNDATIONS OF PERSONALIZED HEALTHCARE

Creating customized ways to prevent, diagnose and treat illnesses is now a main focus in medicine. In personalized healthcare, healthcare is customized by studying the effects of genetic, environmental and lifestyle factors on a person’s health. It is designed to make medical care improve, reduce negative consequences and make the most of available resources, for better outcomes (Collins & Varmus, 2015). Personalized healthcare aims to ensure the correct patient gets the proper treatment when they need it which increases both the quality and efficiency of care.

 

Customized medicine is made possible by big data in medicine which allows the analysis and use of vast amounts of various data. Personal DNA sequences which can be seen from genomic data, display variations that may affect a person’s chances of getting certain diseases and how they react to medications (Ashley, 2016). Along with genes, electronic health records collect large amounts of medical information such as patient histories, diagnoses, lab tests and how patients have been treated. These days, data from wearable devices and health apps supplies information about your heart rate, activity levels, sleep and sugar content (Piwek et al., 2016). With information about diet, environmental settings and social determinants added, these data layers help build a detailed image of the patient.

 

Underlying this change towards personalized health care is the crucial role of artificial intelligence (AI). Machine learning, deep learning and natural language processing (NLP) are some of the techniques used in AI to find meaningful patterns in unstructured data. Machine learning algorithms continue to improve the way they forecast by learning from data all on their own. Since it is inspired by neural networks and is part of machine learning, deep learning is particularly skilled at detecting tough patterns in large or complex data, for example, in medical scans or half-genomes (LeCun, Bengio, & Hinton, 2015). Removing the barriers to processing written texts, natural language processing helps AI find important information in clinical notes, medical records and patient talks (Rajkomar et al., 2019).

 

In addition, AI looks at steady datasets and considers real-time analytics, so it can process input from wearables and sensors to keep tabs on health 24/7 and give immediate alerts when required. By using this feature, a healthcare system can watch out for issues early to make quick actions and alter treatment as required (Shah & Milstein, 2021). Thanks to these AI tools, personalized healthcare relies on making data useful and offers each person a unique way to be treated in medicine.

 

In other words, useful personalized healthcare begins by linking extensive collection of data with advanced AI analysis. Combining personal details about each patient with the decisions and actions needed for their health should greatly improve the practical side of medical care and give patients more control over their well-being.

 

Table II:

 

 

 

AI APPLICATIONS POWERING PERSONALIZATION

Thanks to artificial intelligence (AI), analysis and interpretation of complex data in healthcare are made easier, allowing doctors to shape health plans that work for each patient. AI is having a big impact in the field of genomic medicine. Since there is a vast amount of information in the human genome, figuring out disease risks and deciding on treatments by hand is beyond what humans can do, so computers are needed. Reviewing a wide variety of genetic information quickly with AI helps to find mutations linked to certain diseases, assisting doctors in timely diagnosis and selecting the best therapy (Topol, 2019). This ability matters most when diagnosing rare genetic conditions, because common diagnostic methods usually struggle because these conditions are rare and might present in many different ways. AI also helps pharmacogenomics by predicting both the influence of genetic differences on how drugs are processed and the person’s response to them which in turn helps clinicians offer the right medications to meet a patient’s needs (Relling & Klein, 2011).

 

AI tools are now able to predict diseases in advance, going beyond just focusing on the genome. They look at a mix of genetics, medical records and habits to predict the odds of someone getting diabetes, heart problems or mental illness. For instance, machine learning can anticipate the possibility of type 2 diabetes by looking at body mass index, blood glucose and family history and thereby make it possible to start disease prevention steps earlier (Shickel et al., 2018). AI tools can do the same thing with electrocardiograms and other medical data which helps doctors predict – and therefore prevent – cardiac problems like arrhythmias or heart attacks (Attia et al., 2019). Applications in mental health using AI are being developed to observe the first signs of depression or psychosis in speech patterns, social media posts or behavioral data, helping clinicians interact sooner and giving better care (Gkotsis et al., 2021).

 

With AI, personalized treatment plans get even better by considering what is best for each patient’s particular medical and biological setup. AI systems take different types of data such as genetic findings, stages of disease, other health problems and responses to previous treatment, to help choose the right treatment. In oncology, AI helps oncologists pick the right chemotherapy, immunotherapy or targeted treatment depending on the molecular features of a patient’s tumor (Kourou et al., 2015). Artificial intelligence also helps adjust treatments for ongoing chronic conditions by continuously studying patient data and guessing how their bodies will react to medication or lifestyle changes, thus making sure therapies continue to work and reducing the chance of complications (Esteva et al., 2019).

 

Complementary to the above applications are virtual health assistants that continue to guide and support patients at home and outside regular clinical appointments. Chatbots and digital companions using AI guide patients by reminding them of their medication plan, giving health advice and tracking their symptoms from a distance. They increase the chances that patients take their medicines as directed, save on hospital costs for the system and encourage people to be more ready to manage their health (Bickmore & Picard, 2005). Cognitive behavioral support and constant monitoring of mood and stress can be provided to patients by virtual assistants in mental health which also helps overcome challenges in getting regular care (Fitzpatrick, Darcy, & Vierhile, 2017). By providing continuous information about patients, AI helps clinicians get involved in care early if it’s needed to ensure the best results.

 

All these AI tools demonstrate how the use of data is making personalized healthcare a reality. When applied with genomics, predictive analytics, individualized treatment and digital solutions, AI is changing the way each person is looked after.

 

Table III:

 

AI-DRIVEN TOOLS AND TECHNOLOGIES

Because of the fast progress in artificial intelligence, new powerful tools are now found in medicine, leading to better healthcare. A significant change comes from clinical decision support systems (CDSS) which apply AI to analyze patients’ information and help clinicians decide on diagnosis and treatment steps. The use of electronic health records, results from labs and different images enables these systems to guide treatment for each patient based on their health condition. CDSS makes it easier to use complex information which helps prevent mistakes in diagnosis, ensures therapies are more precise and simplifies the tasks doctors perform, thus improving patient care (Shortliffe & Sepúlveda, 2018). They set an example because they involve AI as a helper in decision-making, not as somebody who replaces healthcare professionals.

 

Supporting decision support systems, new wearable gadgets and sensors are now allowing us to watch health 24/7. Modern devices, including smartwatches and tracking devices, can monitor many details of a person’s health, for example, heart rate, blood oxygen, sleep and activity (Dorsey & Topol, 2020). The algorithms monitor the streaming data to find early signs that someone’s health might deteriorate, automatically adapt and in some cases, predict upcoming implications like arrhythmia or an asthma attack. By constantly monitoring patients, they can take part in their care and healthcare providers can assist them earlier, lowering hospital admissions and making it easier to control long-term diseases (according to Steinhubl et al., 2015). Because of AI and wearable technology, healthcare is becoming more proactive and dynamic.

 

Another major field where AI affects medicine is medical imaging, with AI providing more detailed analysis than simple image interpretation. These new AI algorithms can locate complicated patterns in radiological and pathological images to classify cancers, notice subtle differences and hint at mutations present in different cancers (Esteva et al., 2017). AI improves radiologists’ capabilities, makes diagnosis more accurate and speeds up examining images for personalized treatment choices. Thanks to being fed lots of different kinds of data, these AI models can grow better at handling different conditions and types of X-ray images (Litjens et al., 2017). Using AI with medical imaging shows how personalized medicine is moving towards precise diagnosis.

 

Because of these AI systems, modern personalized medicine takes advantage of clinical decision support systems, wearable devices and more capable imaging technologies. They make it possible for healthcare to change from being reactive to being proactive, precise and focused on each patient’s needs.

 

Table IV:

 

BENEFITS OF PERSONALIZED, AI-POWERED HEALTHCARE

Integrating AI into healthcare on a personal level provides many benefits and greatly transforms the way people are treated in the medical system. Essentially, AI personalization in healthcare has a strong impact on how well patients respond to treatment by delivering the right care at the right moment based specifically on their biological background and lifestyle. Because precision is used, doctors rely less on general treatments that do not always work well and often cause extra side effects. For cancer patients, AI-based genomic studies can help doctors select the right targeted therapies that increase chances of survival and reduce risks of conventional toxicities (Collins & Varmus, 2015). AI makes it easier to detect problems and predict the future course of a disease, helping people receive treatment earlier and enjoy better recovery.

 

Along with better medical results, AI-based healthcare encouraging patients to be more actively involved. Being given personalized information from their health records, patients understand both their conditions and the reasons behind the treatment. Because of this openness, patients tend to be more involved in their health, better follow their treatment plans and make better health choices (McKinney et al., 2020). AI-powered applications and virtual health assistants give patients support and education around the clock which makes it easier for them to be involved in their health decisions. Being more present with patients plays a role in their health and also helps patients and healthcare providers trust each other more.

 

From the view of economics, AI-driven personalized healthcare may bring about better cost management. Often, in traditional medicine, many therapies are tried before the result is what the patient hopes for. The results could be wasted healthcare resources, more prolonged patient suffering and higher healthcare costs. AI’s capability to process complicated information helps doctors decide on proper treatment ahead of time which decreases the number of ineffective or unnecessary procedures (Topol, 2019). Predictive models can also help predict when a patient’s condition might get worse or require hospitalization, so health care providers can act early and prevent costly emergencies (Obermeyer & Emanuel, 2016). All of these improvements help decrease healthcare spending and also make care better.

 

All in all, using AI in healthcare improves medical results, benefits patients and helps reduce costs, making significant improvements toward more personalized care.

 

 

CHALLENGES AND ETHICAL DIMENSIONS

Even though AI-powered personalized healthcare is extremely valuable, introducing these technologies still requires dealing with vital concerns and dilemmas that must be solved sensibly to maintain fairness, safety and trust. Because AI uses a lot of sensitive personal information about health, data privacy and providing consent are major issues. Keeping this data safe and preventing abuse calls for solid security rules and clear laws about consent. People should have complete knowledge about how their data is handled, by who and for what reasons to help them trust medical care (Shen et al., 2019). Handling personal data with care is important for following laws such as GDPR and HIPAA as well as for ensuring patient autonomy and dignity.

 

The fact that AI may learn from biased data leads to what is called algorithmic bias which relates closely to privacy concerns. It can give less precise diagnoses or poor treatment recommendations to populations such as people belonging to racial minorities, women and those who are economically underprivileged (Obermeyer et al., 2019). For example, studies indicate that AI programs created using mainly data from one ethnic group are not effective for other groups, worsening the health gaps rather than fixing them. Fighting bias in algorithms means collecting data carefully, testing them regularly and keeping an eye on how they work in order to ensure that AI systems are fair to everyone.

 

It is possible that AI solutions in healthcare could lead to wealthy groups getting even better care and make the care gap between privileged and poorer groups even wider. In places where resources are limited and where people do not have much digital knowledge, AI tools can be difficult to access because of the costs and the technology they need (Rajkomar et al., 2018). To stop these differences from widening, policymakers, healthcare workers and developers of AI should team up to ensure its use is available, accessible and sensitive to important cultural factors.

 

Last, challenges in how regulations and healthcare providers use AI prevent widespread adoption of AI in personalized healthcare. Even though agencies are developing rules for evaluating AI in medicine, waiting for their approval can still cause delays (FDA, 2021). In addition, creating trust among physicians helps, as some still doubt the use of AI tools because they worry about their transparency, how they work and legal risks (Longoni et al., 2019). Providing detailed training and change management plans in addition to clear regulations helps healthcare workers understand, trust and successfully use AI in their daily tasks.

 

All things considered, using AI for personalized healthcare can be beneficial, yet it is important to face the connected issues of privacy, unfair bias, limited access and being accepted by laws. AI can only make health outcomes better and fairer if it is implemented in an appropriate, inclusive and ethical manner.

 

Table V:

 

CASE STUDIES AND EMERGING INITIATIVES

We are seeing real examples of how AI is helping offer more individualized healthcare in the real world. The advancement of AI in medical care has helped reveal its usefulness as well as its main difficulties in driving personal medical care. Among all of these, Tempus is especially notable for joining clinical and molecular data together to help decision-making in oncology. Tempus gives medical professionals useful insights by using machine learning algorithms with vast amounts of genomic sequencing, imaging and health records. Because of this approach, doctors can better personalize treatment plans and leading cancer centers in the U.S. have adopted it as a result (Tempus, 2023).

 

Using AI is also essential to Deep Genomics which creates new treatments for rare genetic disorders in Canada. Their platform uses big genomic data to identify what causes diseases and also predicts how antisense oligonucleotides might interact with RNA. By using AI, the company has been able to promote candidates to clinical trials, showing that AI can manage the complexities of genetics and optimize drug development (Zhang et al., 2022). The examples demonstrate that because private companies invest in data infrastructure and algorithms, they are ahead in bringing highly targeted AI to healthcare.

 

On the public side, initiatives such as DeepMind Health which Google bought, have made an important impact. DeepMind helped the NHS create an AI system to forecast acute kidney injury (AKI) as many as 48 hours early, aiding timely action and cutting down on deaths. Even though the technical aspects were strong, it made people question data privacy and patient approval, teaching lessons about transparency and trust (Powles & Hodson, 2017). There has been continued AI research in the NHS through NHS AI Lab, a funded body that ensures AI is used safely and appropriately in healthcare, especially supporting health equality and applications in the clinic.

 

They highlight the major differences between public and private sector innovation. Flexible resources and special benefits allow private organisations to progress very fast. But they can be questioned about their transparency and why they profit. It often takes longer, but public initiatives give greater emphasis to equity, accountable decisions and helping the wider group when possible. According to the examples, it seems that meaningful AI use in healthcare may depend on combining the flexibility of private initiatives and the involvement and rules from public institutions (He et al., 2019).

 

Moreover, these examples yield important lessons learned. Besides technology, successful use of AI calls for founded trust, clear guidelines and an inclusive approach. Examples like DeepMind prove that AI with good intentions can fail without proper ethical rules. On the other hand, Tempus and Deep Genomics exemplify that health care gets better when AI is involved and regularly trained with different, high-quality data. They will greatly help as health systems worldwide use AI to make patient care more effective and focused on each patient.

 

THE FUTURE OF PERSONALIZED HEALTHCARE

When artificial intelligence grows, personal healthcare will keep moving forward, getting more helpful, predictive and available to more people. Digital twins which come from engineering, are a key factor in this transformation for medicine. Through the use of someone’s genomic, physiological and behavioral information, a digital twin can be made to virtually show how the person might react to different actions or conditions. Using precise computer simulations, clinicians can check different strategies and decide if they are safe and effective before using them in real life. For example, scientists have modelled the heart response to inputs and predicted atrial arrhythmias using individual patient’s electrophysiology (Corral-Acero et al., 2020). As they are made more accurate, these models may offer major improvements in how drugs are developed, operations are planned and people manage chronic health problems, by providing more personalized care.

 

Also, federated learning will play a major role in the growth of AI in healthcare, as it trains models with data that remains protected at its original source such as in hospitals and clinics. It makes data privacy and compliance much stronger, ensuring institutions can share information without revealing personal patient details. Traditional AI systems in health care are not allowed to share too much data widely, due to privacy and legal rules. With federated learning, algorithms are passed to individual datasets and the changes to the model are sent back together, not the raw data itself. With the NVIDIA Clara platform and collaboration with King’s College London, it has been shown that using federated learning on radiology data from multiple hospitals is possible, reducing risks to data and still resulting in effective medical models (Sheller et al., 2020). Collaboration and legal rules in this area will allow federated learning to become critical to large-scale AI work that respects privacy in healthcare.

It is important that moving forward, global cooperation will make sure personalized healthcare from AI is open to all and fair. Today’s AI tools tend to be biased because data sets rarely cover all demographics equally which causes them to make less precise predictions for underrepresented groups. International leaders should work on including many diverse individuals in datasets and ensure knowledge about how to make models equally accessible is widely shared. Cross-national groups like the Global Alliance for Genomics and Health (GA4GH) are helping unite genomic and health data standards and encouraging more research in AI, while lessening gaps in health outcomes (Knoppers, 2014). Taking a global, team effort helps ensure fairness and also allows solutions in healthcare to better serve people from all walks of life.

 

All in all, the progress of personalized healthcare depends on three major shifts: using digital twins for smart simulations, achieving privacy and scale through federated learning and making sure all people can be included by sharing various kinds of data. All in all such advancements should allow AI to shape medicine in a way that is both preemptive, precise and available to everyone.

 

CONCLUSION

As AI joins the healthcare industry, toronto patients now experience more precise, fairer and personalized treatment. Working with advanced machine learning, natural language processing and predictive algorithms, data has helped us discover both commonalities and unique features in large clinical and genomic data. Through precision diagnosis, custom treatment, risk prediction and patient participation, AI moves medicine away from just using new gadgets and towards a new outlook on how healthcare should be provided.

 

AI in healthcare is more complex than just being about speed or accurate predictions. It also focuses on the story of humans such as designing the tech, using it and putting it into action. With these challenges, it is important to work towards AI development that emphasizes honesty, safeguards people’s privacy and reduces unfair bias in algorithms. For efforts to succeed, people from many disciplines must join together such as clinicians, technology experts, ethicists, policymakers and patients. Innovation should focus on patients as more than just a catchy phrase; it should steer the design, setup and evaluation of all aspects of care.

 

In the future, the goal should be not just to use digital systems, but to rebuild medicine with empathy and inclusion at the heart. The main advantage of AI is in influencing a model where every person is understood as their own set of data, biology, behaviors and context. If we add fairness, equity and compassion to smart systems in medicine, it ensures the next era of medical care will be both smart and respectful of patients.

 

In this view, AI works together with humans in healthcare to make things better. It acts as a helper in medical care, reveals how complicated things can be understood and brings us closer to an age when everyone can use the best scientific tools. To redesign medicine as such, we need to be able to innovate and also look after our moral duties in medicine.

 

 

REFERENCES

1. Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507–522. https://doi.org/10.1038/nrg.2016.86 

2. Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., ... & Noseworthy, P. A. (2019). Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nature Medicine, 25(1), 70–74. https://doi.org/10.1038/s41591-018-0240-2 

3. Bickmore, T. W., & Picard, R. W. (2005). Establishing and maintaining long-term human-computer relationships. ACM Transactions on Computer-Human Interaction, 12(2), 293–327. https://doi.org/10.1145/1067860.1067867 

4. Collins, F. S., & Varmus, H. (2015). A new initiative on precision medicine. The New England Journal of Medicine, 372(9), 793–795. https://doi.org/10.1056/NEJMp1500523 

5. Corral-Acero, J., Margara, F., Marciniak, M., Rodero, C., Loncaric, F., Feng, Y., ... & Lamata, P. (2020). The ‘Digital Twin’ to enable the vision of precision cardiology. European Heart Journal, 41(48), 4556–4564. https://doi.org/10.1093/eurheartj/ehaa159 

6. Dorsey, E. R., & Topol, E. J. (2020). Telemedicine 2020 and the next decade. The Lancet, 395(10227), 859. https://doi.org/10.1016/S0140-6736(20)30424-4 

7. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056 

8. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z 

9. Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785 

10. Gkotsis, G., Oellrich, A., Velupillai, S., Liakata, M., Hubbard, T., Dobson, R., & Dutta, R. (2021). The role of artificial intelligence in mental health research: A systematic review. NPJ Digital Medicine, 4(1), 1–12. https://doi.org/10.1038/s41746-021-00406-3 

11. Hamburg, M. A., & Collins, F. S. (2010). The path to personalized medicine. The New England Journal of Medicine, 363(4), 301–304. https://doi.org/10.1056/NEJMp1006304

12. He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0 

13. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101 

14. Knoppers, B. M. (2014). Framework for responsible sharing of genomic and health-related data. The HUGO Journal, 8(1), 3. https://doi.org/10.1186/1877-6566-8-3 

15. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17. https://doi.org/10.1016/j.csbj.2014.11.005 

16. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539 

17. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.  https://doi.org/10.1016/j.media.2017.07.005

18. McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., ... & Suleyman, M. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94.  https://doi.org/10.1038/s41586-019-1799-6 

19. Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181 

20. Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The rise of consumer health wearables: Promises and barriers. PLoS Medicine, 13(2), e1001953. https://doi.org/10.1371/journal.pmed.1001953 

21. Powles, J., & Hodson, H. (2017). Google DeepMind and healthcare in an age of algorithms. Health and Technology, 7(4), 351–367. https://doi.org/10.1007/s12553-017-0179-1 

22. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259 

23. Relling, M. V., & Klein, T. E. (2011). CPIC: Clinical pharmacogenetics implementation consortium of the pharmacogenomics research network. Clinical Pharmacology & Therapeutics, 89(3), 464–467.  https://doi.org/10.1038/clpt.2010.279 

24. Shah, N., & Milstein, A. (2021). Wearable technology and AI: Transforming chronic disease management. Journal of Medical Internet Research, 23(3), e24300. https://doi.org/10.2196/24300 

25. Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., & Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10(1), 12598. https://doi.org/10.1038/s41598-020-69250-1 

26. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1603. https://doi.org/10.1109/JBHI.2017.2767063 

27. Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical decision support in the era of artificial intelligence. JAMA, 320(21), 2199–2200. https://doi.org/10.1001/jama.2018.17163 

28. Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2015). The emerging field of mobile health. Science Translational Medicine, 7(283), 283rv3. https://doi.org/10.1126/scitranslmed.aaa3487

29. Tempus. (2023). About Tempus. Retrieved from https://www.tempus.com 

30. Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

31. Zhang, Y., Adachi, A., & Frey, B. J. (2022). AI-powered discovery of genetic medicines for rare diseases. Nature Biotechnology, 40(6), 768–777. https://doi.org/10.1038/s41587-022-01307-y