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5 ways oncology professionals can tap into AI today

By | 2 minute read | July 12, 2019

Over the last few years, experts have predicted that solutions featuring artificial intelligence (AI) would help oncology professionals in their efforts to advance research, improve clinical outcomes, and optimize their operational processes.1 Good news: while there is still progress to be made, we believe AI is having an impact on the decision-making of oncology professionals today.

Here are five ways AI is already changing the oncology landscape:

  1. Medical literature contains a wealth of knowledge that can benefit oncology professionals, if they can access the insights in a timely manner. AI-enabled technology has demonstrated its ability to identify relevant clinical publications from biographic databases, without relying on expert curation or bibliometric methods.2
  2. AI-enabled tools learn extremely complex relationships and are well suited to learn from the complex data that has been generated from modern clinical care.3 This means that with AI solutions professionals can access insights from multiple sources, including medical notes entered by physicians, medical images, continuous monitoring data from sensors, and genomic data.4
  3. Today, AI-enabled solutions are helping clinicians and researchers match patients with appropriate clinical trials. This is because machine learning tools – of which AI solutions are a subset — are trained on a plethora of data points from patient charts in EHRs and can then pull-out relevant information without any lapses of attention.5
  4. Machine learning solutions can learn the patterns of health trajectories for a vast number of patients and organizations. This can help leaders anticipate future needs and take steps to prepare. This is especially important as community health systems are working to stay on top of the growing incidence of cancer and efficiently meet their patients’ needs.6
  5. Reducing clinical variation and related costs requires the analysis of massive amounts of data – often spread across multiple systems. It can be challenging for traditional analytics to do this.

AI solutions feature iterative, complex pattern matching capabilities, which can perform tasks, at a speed and scale that exceed human capability. A handful of sophisticated AI decision support tools also incorporate training and knowledge form leading oncology organizations. These types of solutions, can help clinicians and patients make more informed decisions together.7

To address challenges involved in of cancer care today, IBM Watson Health has developed a suite of products to support professionals in their work across the cancer continuum.

Learn more about IBM Watson Health in Oncology

  2. Use of Machine Learning to Identify Relevant Research Publications in Clinical Oncology, Fernando Suarez Saiz, et al. (2019 Watson Health Study)
  3. Rajkomar, A., et al. PMC 2019. Machine Learning in Medicine.
  4. Rajkomar, A., et al. PMC 2019. Machine Learning in Medicine.
  5. Rajkomar, A., et al. PMC 2019. Machine Learning in Medicine.
  7. Bowles, K., et al. NCBI – PMC 2015. The Use of Health Information Technology to Improve Care and Outcomes for Older Adults
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