Artificial Intelligence Failure at IBM ‘Watson for Oncology’.

The case discusses the failure of International Business Machines’ (IBM) artificial intelligence (AI) software Watson for Oncology (Watson). In 2012, the American multinational technology company partnered with New York-based cancer treatment center and research organization Memorial Sloan Kettering Cancer Center (MSK) to develop Watson that could provide medical professionals with improved access to up-to-date and comprehensive cancer data and practices. Despite Watson’s Natural Language Processing (NLP) ability that enabled it to read and gain insights from unstructured data it was not able to interpret data as human doctors could. A 2017 investigation carried out by news website STAT revealed how IBM’s AI software could not live up to the hype created around it by the company. To add to IBM’s troubles, a 2017 audit carried out by the University of Texas showed that MD Anderson was using old data to train its OEA. The same year, i.e. in 2017, the cancer center closed down its project with IBM after spending $62 mn. With data quality and domain expertise being some of the major reasons for the failure of AI projects, is it worth it for companies such as IBM to invest in AI and Machine Learning (ML) tools in healthcare like Watson for Oncology without proper data preparation? This study looks at the roadblocks standing in the way of IBM finding success in using AI and ML in a bid to drive clinical research and drug discovery. It also examines how technology companies can make AI and ML a truly transformative force in healthcare. [ABSTRACT FROM AUTHOR]