8 Strategies for Developing Artificial Intelligence Systems for Healthcare Applications

The complexity and increase in data in healthcare mean that artificial intelligence (AI) is increasingly being applied in this field. Payers, care providers, and life science companies are already employing several types of AI, including diagnostic and treatment recommendations, patient participation and compliance, and administrative activities. While AI can perform healthcare tasks as well or better than humans, implementation factors will prevent the large-scale automation of the jobs of health professionals for a significant period of time. Ethical issues in the application of AI to healthcare are also discussed.

Some of the most promising use cases for AI tools include predictive analysis, precision medicine, and clinical decision support. Development in all of these areas is already underway. We focus on the computational scientist as the primary audience and emphasize that AI must be purposefully designed to improve long-standing systemic challenges in healthcare. We describe a non-exhaustive set of applications of AI in health care in the short, medium and long term, to learn about the possible capabilities of AI to augment, automate and transform medicine.

In addition, healthcare organizations and doctors' offices will move from adopting AI platforms to becoming co-innovators with technological partners in the development of new AI systems for precision therapeutics. Montefiore Medical Center, Partners HealthCare and CancerLinq, of the American Society for Clinical Oncology, are just a few examples of projects focused on healthcare that use machine learning techniques and semantic computing to create semantic computing systems that can support collaborative research, predictive analysis, support for clinical decision-making and, perhaps, eventually the transition to what could be considered artificial intelligence. Since AI in healthcare is a rapidly evolving field, it is important to consider the ethical implications of its use. It is also important to understand the potential impact on healthcare professionals in Europe.

A series of individual interviews with 62 healthcare leaders and other leaders with experience in AI and digital health, as well as an online survey of 175 health professionals, investors in the healthcare sector and founders of AI startups and other executives have provided insight into this issue. In order for AI to develop its full potential in European healthcare, there are several preconditions that must be met. These include the integration of broader data sets in all organizations, strong governance to continuously improve data quality, and greater confidence on the part of organizations, professionals and patients both in AI solutions and in the ability to manage related risks. It may still be a few years before doctors can sit back and relax while their robotic assistants are dedicated to diagnosing their patients, but there are already several examples of AI companies helping the healthcare industry stay afloat in an ocean of data.

IBM has been dedicating health data to Watson for several years and has disbursed billions of dollars to acquire big data analysis companies that will promote its goal of creating a truly intelligent partner for quality care. When developing an artificial intelligence system for healthcare applications, it is important to consider a number of strategies. These include understanding ethical implications; analyzing impact on healthcare professionals; integrating broader data sets; implementing strong governance; increasing confidence in AI solutions; leveraging machine learning techniques; utilizing semantic computing systems; and partnering with technological companies. By following these strategies, organizations can ensure that they are taking advantage of all the potential benefits that artificial intelligence has to offer.

Deanna Trueman
Deanna Trueman

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