Medicine, even from the earliest days of artificial intelligence (AI) research, has been one of the most inspiring and promising domains for the application of AI-based approaches. Equally, it has ...been one of the more challenging areas to see an effective adoption. There are many reasons for this, primarily the reluctance to delegate decision making to machine intelligence in cases where patient safety is at stake. To address some of these challenges, medical AI, especially in its modern data-rich deep learning guise, needs to develop a principled and formal uncertainty quantification (UQ) discipline, just as we have seen in fields such as nuclear stockpile stewardship and risk management. The data-rich world of AI-based learning and the frequent absence of a well-understood underlying theory poses its own unique challenges to straightforward adoption of UQ. These challenges, while not trivial, also present significant new research opportunities for the development of new theoretical approaches, and for the practical applications of UQ in the area of machine-assisted medical decision making. Understanding prediction system structure and defensibly quantifying uncertainty is possible, and, if done, can significantly benefit both research and practical applications of AI in this critical domain.Arguably one of the most promising as well as critical applications of deep learning is in supporting medical sciences and decision making. It is time to develop methods for systematically quantifying uncertainty underlying deep learning processes, which would lead to increased confidence in practical applicability of these approaches.
Dramatic disruptions in technologies can result in the creation of new markets, the emergence of new paradigms, and the displacement of entrenched approaches and business models. This can be ...especially pronounced when multiple technologies converge to create something new. Today, artificial intelligence (“AI”) is acting as an accelerant for such technology transformations. Still, the scope and depth of its impact will depend in part on our ability to address challenging problems that are surfacing without solutions. Industries will pursue AI for their corporate missions and to create shareholder value. But the timescales and applications can be incongruent with important public needs and with the longer research horizons required to make real progress. Public-Private partnerships are an important mechanism for tackling these rapidly emerging new, complex, challenging problems. This paper examines our experience gained by creating a unique public-private partnership to apply AI to one example of a hard problem that is particularly timely today-- dramatically accelerating drug discovery.
The fields of science have undergone dramatic reorganizations as they have come to terms with the realities of the growing complexities of their problem set, the costs, and the breadth of skills ...needed to make major progress. A field such as particle physics transformed from principal investigator‐driven research supported by an electron synchrotron in the basement of your physics building in the 1950s, to regional centers when costs became prohibitive to refresh technology everywhere, driving larger teams of scientists to cooperate in the 1970s, to international centers where multinational teams work together to achieve progress. The 2013 Nobel Prize winning discovery of the Higgs boson would have been unlikely without such team science. Other fields such as the computational sciences are well on their way through such a transformation. Today, we see precision medicine as a field that will need to come to terms with new organizational principles in order to make major progress, including everyone from individual medical researchers to pharma. Interestingly, the Cancer Moonshot has helped move thinking in that direction for part of the community and now the initiative has been transformed into law.
Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the ...entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.
Medicine is, in its essence, decision making under uncertainty; the decisions are made about tests to be performed and treatments to be administered. Traditionally, the uncertainty in decision making ...was handled using expertise collected by individual providers and, more recently, systematic appraisal of research in the form of evidence-based medicine. The traditional approach has been used successfully in medicine for a very long time. However, it has substantial limitations because of the complexity of the system of the human body and healthcare. The complex systems are a network of highly coupled components intensely interacting with each other. These interactions give those systems redundancy and thus robustness to failure and, at the same time, equifinality, that is, many different causative pathways leading to the same outcome. The equifinality of the complex systems of the human body and healthcare system demand the individualization of medical care, medicine, and medical decision making. Computational models excel in modeling complex systems and, consequently, enabling individualization of medical decision making and medicine. Computational models are theory- or knowledge-based models, data-driven models, or models that combine both approaches. Data are essential, although to a different degree, for computational models to successfully represent complex systems. The individualized decision making, made possible by the computational modeling of complex systems, has the potential to revolutionize the entire spectrum of medicine from individual patient care to policymaking. This approach allows applying tests and treatments to individuals who receive a net benefit from them, for whom benefits outweigh the risk, rather than treating all individuals in a population because, on average, the population benefits. Thus, the computational modeling–enabled individualization of medical decision making has the potential to both improve health outcomes and decrease the costs of healthcare.
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