Article series from the Nordic Artificial Intelligence Institute (www.nordicAIinstitute.com).
Introduce a map, overview and guide describing significant areas of AI and Health Care/Medicine
Explain briefly scientific disciplines contributing to Artificial Intelligence
Discuss practical AI technology areas that transform Health Care/Medicine
Artificial Intelligence (AI) is a term coined by John McCarthy in 1956. This also marked the beginning of many substantial scientific and industrial AI activities (including the IJCAI conference series - the world's premier scientific conference on AI). But more so, AI has become a dominant transformational driver in the health industry and the public sector. This AI transformation comes at a good time - health care systems face huge challenges in continuing to deliver quality of care - one reason is the increase of chronic diseases.
The good news is that AI offers substantial benefits to the health care and medical field. AI is becoming increasingly accurate and effective in performing a broad range of complex health care related tasks (e.g. recognizing a malignant tumor on MRIs). Such AI driven performance can then be scaled up, improve health outcomes and save many lives. As a result, AI helps clinicians, patients and many health stakeholders to make faster, better, and cheaper decisions, at scale. Figure 1 shows a map of some of the primary areas of AI and Health Care/Medicine.
Figure 1: An introduction map, overview and guide of Artificial Intelligence in Health Care and Medicine.
The intersection of AI and Health Care & Medicine is a rich field of science, technology and innovation. As seen on the left in Figure 1, scientific areas that contributed to the field of Artificial Intelligence are Philosophy, Neuroscience, Health, Medicine, Mathematics, Computer Science, Economy and Engineering. For example, philosophy supports our understanding of key concepts in AI such as the philosophy of mind and "free will". Neuroscience and AI have been intertwined for decades, resulting in concepts such as Artificial Neural Networks. There are of course many more scientific disciplines than those listed in Figure 1, and each contributes in different ways to the progression of AI.
AI has brought forward many advanced technologies, and is used in many companies and organisations (see e.g. the recent Health 2.0 conference in California). Figure 1 names just a few in the context of the health care and medical field, discussed here in more detail.
Robotics Applications in health include robotic surgeons (robots perform basic surgery autonomously and learn from human surgeons), medical drones (delivering blood and defibrillators on demand), robotic nurses and companion robots -- medical cobots.
Machine Perception AI technologies that use their "sensors" similar to the way humans use their senses. This may include the recognition of malignant tumors in MRIs, and "smelling" if you are sick based on a person's odorprint.
Natural Language Processing Related areas are computational linguistics, natural language understanding, and multi modal interactions (including e.g. facial expression and intonation). These have resulted in application such as chatbots used at the NHS in the United Kingdom to answer medical questions, understanding unstructured medical notes into standardised electronic health records, and suggest treatment options based on understanding millions of scientific medical publications. None of these tasks could be done by health care professionals at scale.
Machine Learning: Due to a massive increase of data about the human condition (e.g. physiological and behavioural information) and trying to make use of this data, machine learning has received a lot of attention in recent years (e.g. related areas are analytics, data science, data mining, approaches which still need a human in the loop). Machine Learning applications are vast, e.g. classifying DNA sequences and medical diagnosis. This area also comes with fundamental ethical questions not currently addressed, e.g. digitising cultural prejudices.
Multi Agent Systems: Coordination and collaboration of multiple actors is critical in many health care settings. An AI system supports care teams that often consists of multiple health care providers, carers, and the patient themselves. The AI models used in Multi Agent systems allow for smooth coordination among several entities, such as individuals, clinics, and authorities. This area also discusses the team interaction of mixed human-AI teams.
AI is much broader and deeper than explained in this article. Some countries and organisations are well ahead in the era of AI, while others are just at the beginning. It is clear that countries and organisations placing AI clearly on their roadmap, have a strong AI strategy, and are able to execute on it, will have a better position in the future. We soon publish an article on which organisations are ahead in the AI era -- stay tuned.
We continue this article series on Artificial Intelligence. Stay informed by simply following Christian Guttmann on Linkedin and Twitter, and follow the Nordic Artificial Intelligence Institute. With great dedication, Christian has been leading themes and projects in each of the above AI areas over the last 20 years. If you would like to discuss this topic, please reach out and leave your comments below.
Christian Guttmann (PhD) is the executive director of the Nordic Artificial Intelligence Institute, CEO of HealthiHabits AB, Associate Professor (Adj.) at the University of New South Wales, and Research Fellow (Adj.) at the Karolinska Institute. With over 20 years of experience in leading international projects on Artificial Intelligence (AI) in Health Care and Medicine, his expertise is in the intersection of research, innovation and industrial/commercial application of AI. He has a PhD in Artificial Intelligence, and several degrees in Artificial Intelligence and Psychology.