Another aspect of big data is the information produced by wearables and within social media channels.4 Depending on the device and applications used by the individual, wearables can generate both behavioral and physiologic data that could be mined for insight on interventions — such as modifying behavior to reduce the risk of cancer or identifying adverse events.

A current limitation for wearables is their accuracy in measuring physiologic variables. Social media also generates a vast amount of behavioral data, and studies suggest that the types of posts that are made, the language and tone that is used, and the searches that are performed can also be mined by AI to identify people who may be experiencing symptoms related to a medical condition.

Clinical Decision Tools

As the understanding of cancer continues to grow, and numerous new therapies are being approved, the complexity of cancer management has dramatically increased.5 As a result, AI is being developed to support clinicians in their decision making, with the goal of improving quality of care.5,6 This may require the use of big data to identify meaningful patterns or to help researchers come to conclusions, but the use of those data would be to create a tool that would help clinicians make decisions about cancer treatment.


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An example of this is an AI platform that provides clinical decision-making support by making treatment suggestions.7 The platform bases its suggestions on clinical practice guideline recommendations, analysis of the scientific literature, learning from experts and test cases, and the patient’s characteristics. An early study of this platform showed that the AI technology chose treatments that were highly concordant with what prostate cancer specialists would select.

In addition, electronic health records and other databases or registries are rich sources of real-world data.6 AI analysis of these data can be used to help clinicians and their institutions make better decisions. For example, the American Society of Clinical Oncology developed CancerLinQ, which houses electronic health record data from participating institutions, and then uses AI to analyze these data — data that members of the industry group can access for research purposes.6,7

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Conclusions

There are many potential applications of AI that are being developed to support oncologists in the areas of cancer screening and diagnosis, processing and analyzing big data, and clinical decision making. AI platforms are already in use in some areas of oncology, including for support in the screening and diagnosis of cancer, for the identification of biomarkers or treatment trends, and for the evaluation of large databases.

References

  1. Yu K-H, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-731.
  2. Data Science Institute. American College of Radiology. FDA cleared AI algorithms. Accessed May 5, 2020.
  3. Arterys Inc. Lung AI website. https://arterys.com/lung-ai/. Accessed May 5, 2020.
  4. Wang F, Preininger A. AI in health: state of the art, challenges, and future directions. Yearb Med Inform. 2019;28(1):16-26.
  5. Yu SH, Kim MS, Chung HS, et al. Early experience with Watson for Oncology: a clinical decision‑support system for prostate cancer treatment recommendations [published online April 25, 2020]. World J Urol. doi: 10.1007/s00345-020-03214-y
  6. Kantarjian H, Yu PP. Artificial intelligence, big data, and cancer. JAMA Oncol. 2015;5:573-574.
  7. American Society of Clinical Oncology. ASCO CancerLinQ. Website. https://www.cancerlinq.org/solutions/oncology-practices. Accessed May 5, 2020.