Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing

Patel, Nirali M.; Michelini, Vanessa V.; Snell, Jeff M.; Balu, Saianand; Hoyle, Alan P.; Parker, Joel S.; Hayward, Michele C.; Eberhard, David A.; Salazar, Ashley H.; McNeillie, Patrick; Xu, Jia; Huettner, Claudia S.; Koyama, Takahiko; Utro, Filippo; Rhrissorrakrai, Kahn; Norel, Raquel; Bilal, Erhan; Royyuru, Ajay; Parida, Laxmi; Earp, H. Shelton; Grilley‐Olson, Juneko E.; Hayes, D. Neil; Harvey, Stephen J.; Sharpless, Norman E.; Kim, William Y.

doi:10.1634/theoncologist.2017-0170

Cited by 90 publications

(75 citation statements)

References 16 publications

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“…In another study, Watson found cancer treatments that oncologists overlooked, by discovering ‘potential therapeutic options’ for 323 additional patients after analysing ‘large volumes of data’, including past studies, databases and genetic information 37. It should be noted that these superior performances in the theoretical setting has not translated well into real-world clinical practice, based on recent reports of poor clinician adoption at a major American cancer centre 38…”

Section: Resultsmentioning

confidence: 99%

Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health

Loh

2018

leader

110

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Artificial intelligence (AI) has the potential to significantly transform the role of the doctor and revolutionise the practice of medicine. This qualitative review paper summarises the past 12 months of health research in AI, across different medical specialties, and discusses the current strengths as well as challenges, relating to this emerging technology. Doctors, especially those in leadership roles, need to be aware of how quickly AI is advancing in health, so that they are ready to lead the change required for its adoption by the health system. Key points: ‘AI has now been shown to be as effective as humans in the diagnosis of various medical conditions, and in some cases, more effective.’ When it comes to predicting suicide attempts, recent research suggest AI is better than human beings. ‘AI’s current strength is in its ability to learn from a large dataset and recognise patterns that can be used to diagnose conditions, putting it in direct competition with medical specialties that are involved in diagnostic tests that involve pattern recognition, such as pathology and radiology’. The current challenges in AI include legal liability and attribution of negligence when errors occur, and the ethical issues relating to patient choices. ‘AI systems can also be developed with, or learn, biases, that will need to be identified and mitigated’. As doctors and health leaders, we need to start preparing the profession to be supported by, partnered with, and, in future, potentially be replaced by, AI and advanced robotics systems.

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Section: Resultsmentioning

confidence: 99%

Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health

Loh

2018

leader

110

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“…As oncologic care increasingly relies on molecular characteristics, additional decision support may be needed . The companion solution, Watson for Genomics (WfG), which analyzes and categorizes genetic alterations that are related to cancer progression and suggests potential treatments, was able to identify actionable mutations in one‐third of patients compared to a university molecular tumor board . A recent WfG review in Spain found investigational options were identified in most cases from a variety of samples …”

Section: Discussionmentioning

confidence: 99%

Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists

McNamara

Goldberg²,

Latts

et al. 2019

Cancer Medicine

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Introduction Cognitive computing point‐of‐care decision support tools which ingest patient attributes from electronic health records and display treatment options based on expert training and medical literature, supplemented by real world evidence (RWE), might prove useful to expert and novice oncologists. The concordance of augmented intelligence systems with best medical practices and potential influences on physician behavior remain unknown. Methods Electronic health records from 88 breast cancer patients evaluated at a USA tertiary care center were presented to subspecialist experts and oncologists focusing on other disease states with and without reviewing the IBM Watson for Oncology with Cota RWE platform. Results The cognitive computing “recommended” option was concordant with selection by breast cancer experts in 78.5% and “for consideration” option was selected in 9.4%, yielding agreements in 87.9%. Fifty‐nine percent of non‐concordant responses were generated from 8% of cases. In the Cota observational database 69.3% of matched controls were treated with “recommended,” 11.4% “for consideration”, and 19.3% “not recommended.” Without guidance from Watson for Oncology (WfO)/Cota RWE, novice oncologists chose 75.5% recommended/for consideration treatments which improved to 95.3% with WfO/Cota RWE. The novices were more likely than experts to choose a non‐recommended option (P < .01) without WfO/Cota RWE and changed decisions in 39% cases. Conclusions Watson for Oncology with Cota RWE options were largely concordant with disease expert judged best oncology practices, and was able to improve treatment decisions among breast cancer novices. The observation that nearly a fifth of patients with similar disease characteristics received non‐recommended options in a real world database highlights a need for decision support.

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“…Cognitive Computing is an overall awareness offering Artificial intelligence as figuring abilities which can be functional to enhance/ increase human cognition, maintain balances and imitates human knowledge in cognitive-task presentation. Cognitive computing contains AI knowledge and techniques that includes understanding of natural language, machine learning, speech and image recognition, familiar human interface [8,9].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Behavioural Therapy to Compute Psychological Illness of Students by Using an Expert System

Jabeen¹,

Sandhu²,

Tahir³

et al. 2018

J Depress Anxiety

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Objective: This research is being conducted to develop a technological solution for mentally distorted students. Though the mental health of university students is known globally as a momentous public health matter. Academicals and social stresses are playing quite a negative role in university student's life, especially in forms of mental illness like stress, depression, and anxiety. These mental health issues are becoming a major constraint towards their studies and career. Method: Psychologist used different scales to measure a level of mental disorder. However, to measure such a disease level, we are working on a knowledge-based expert system that will be used to compute its level among the students who are affiliated with technological studies. Mostly psychologist does psychotherapy and use other instruments to cure such a patient for which they must have to visit the psychologist. However, if the psychologist is not available especially in remote areas then the expert system can be used as reciprocal. In order to make our expert system more validate and authentic the knowledge of psychological expert will be used under the process of development. Results: Data from 500 technological University Students are collected from one of the universities in Sialkot, Pakistan. Almost more than 200 students remained clear minded and fall under the normal state of depression and 122 students in case of Anxiety remained normal. 206 students out of 500 were responders to the abnormal stage of anxiety on a 5-point scale from an average of 4.5 points. On the basis of this, an expert system is being designed to facilitate the students. Conclusion: As per results, 30%-35% students were in the range of abnormality. Therefore, we are further going to develop an evaluation mechanism by using technological ways so that an expert system can replace a psychologist.

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Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing

Cited by 90 publications

References 16 publications

Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health

Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health

Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists

Cognitive Behavioural Therapy to Compute Psychological Illness of Students by Using an Expert System

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