This research report presents a proof-of-concept study on the application of machine learning techniques to video and speech data collected during diagnostic cognitive assessments of children with a neurodevelopmental disorder. The study utilised a dataset of 39 video recordings, capturing extensive sessions where clinicians administered, among other things, four cognitive assessment tests. From the first 40 minutes of each clinical session, covering the administration of the Wechsler Intelligence Scale for Children (WISC-V), we extracted head positions and speech turns of both clinician and child. Despite the limited sample size and heterogeneous recording styles, the analysis successfully extracted path signatures as features from the recorded data, focusing on patient-clinician interactions. Importantly, these features quantify the interpersonal dynamics of the assessment process (dialogue and movement patterns). Results suggest that these features exhibit promising potential for predicting all cognitive tests scores of the entire session length and for prototyping a predictive model as a clinical decision support tool. Overall, this proof of concept demonstrates the feasibility of leveraging machine learning techniques for clinical video and speech data analysis in order to potentially enhance the efficiency of cognitive assessments for neurodevelopmental disorders in children.

Many are aware of the Cancer Moonshot--an ambitious and hopeful initiative of the U.S. government to reduce cancer-related death rates by 50% by the year 2047. It will take an army to achieve this goal, composed of the brightest minds and biggest hearts in healthcare, science, and technology. Many parties will be involved--the federal government, healthcare providers, researchers, patients, caregivers, and advocates, among others in both the public and private sectors. One of the most pivotal tools that can help propel us toward this lofty goal is artificial intelligence (AI), which is poised to revolutionize cancer treatment. The moonshot plan identifies five priority areas, all of which AI has the potential to enhance. Two areas in particular lend themselves to AI: the call to "deliver the latest cancer innovations to patients and communities" and the aim of enhancing "the oncology model to place cancer patients at the center of decision-making."

Artificial intelligence (AI) and machine learning (ML) are poised to transform the way health care is delivered. AI is the use of computers to simulate intelligent tasks typically performed by humans. ML is a domain of AI that involves computers automatically learning from data without a priori programming. While AI has been critiqued as being in its "hype cycle" (throughout this article, AI will be used as shorthand for AI and ML), over time, it is likely that every medical specialty will be influenced by AI, and some will be transformed.1 As AI takes on a larger role in clinical practice, it is clear that multiple levels of oversight are needed. However, even with appropriate outside oversight, the importance of clinician review and trust of these technologies cannot be overstated.

This report explores the opportunities for automation and AI in health care and the challenges of deploying them in practice. It draws on learning from the Health Foundation's programmes and research – including a recent study by the University of Oxford on the potential of automation in primary care – as well as a range of other literature. Informed by YouGov online surveys of more than 4,000 UK adults and more than 1,000 NHS staff, the report finds that while automation and AI hold huge potential for improving care and supporting the NHS to increase its productivity, in developing and deploying them we must be careful not to squeeze out the human dimension of health care, and must support the health and care workforce to adapt to and shape technological change. Our surveys found public and NHS staff opinion divided on whether automation and AI in health care are a good or bad thing. Government and NHS leaders must therefore engage with the public and NHS workforce to raise awareness of and build confidence in technology-enabled care.

One of the dramatic trends at the intersection of computing and healthcare has been patients' increased access to medical information, ranging from self-tracked physiological data to genetic data, tests, and scans. Increasingly however, patients and clinicians have access to advanced machine learning-based tools for diagnosis, prediction, and recommendation based on large amounts of data, some of it patient-generated. Consequently, just as organizations have had to deal with a "Bring Your Own Device" (BYOD) reality5 in which employees use their personal devices (phones and tablets) for some aspects of their work, a similar reality of "Bring Your Own Algorithm" (BYOA) is emerging in healthcare with its own challenges and support demands. BYOA is changing patient-clinician interactions and the technologies, skills and workflows related to them. Situations in which patients have direct access to algorithmic advice are becoming commonplace.4