The reviewers are all enthusiastic about the paper, though by varying degrees. The paper's main significant contribution is to the problem of planning for a class of partially observed restless bandits with two arm states each, for which a monotone transition probability structure holds -- the paper argues that this structure is quite natural in several applications, and demonstrates numerical results on one such setting involving medical interventions. It is shown that under this structure, the restless bandit is Whittle-indexable. Although there is no learning component addressed in the paper, the hope is that such a structural characterization will open up avenues for more work on learning good policies when there is a priori uncertainty about the restless Markov decision processes.

Artificial intelligence (AI) has potential to drive game-changing improvements for underserved communities in global health. In response, The Rockefeller Foundation and USAID partnered with the Bill and Melinda Gates Foundation to develop AI in Global Health: Defining a Collective Path Forward. Research began with a broad scan of instances where artificial intelligence is being used, tested, or considered in healthcare, resulting in a catalogue of over 240 examples. This grouping involves tools that leverage AI to monitor and assess population health, and select and target public health interventions based on AI-enabled predictive analytics. It includes AI-driven data processing methods that map the spread and burden of disease while AI predictive analytics are then used to project future disease spread of existing and possible outbreaks.

Researchers and provider organisations have increasingly embraced artificial intelligence (AI) and machine learning (ML) tools to reduce and track the spread of COVID-19 and to improve their surveillance efforts. Big data analytics systems have helped health experts to stay ahead of the pandemic from predicting patient outcomes to anticipating future hotspots, resulting in more efficient care delivery. However, the level of pandemic preparation by healthcare organisations is only as good as the data available to them. Although the industry is well aware of the data issues, the COVID-19 pandemic has brought a host of unique challenges to the forefront of care delivery. Nature of the SARS-CoV-2 has led to significant gaps in COVID-19 data with inconsistencies in information, leaving officials uncertain of the effectiveness of public health interventions.

The founder of an up and coming artificial intelligence (AI) company said targeting disease is a challenging if not herculean process, and it will take the science and skills of AI to conquer it. But it also takes a ton of money. That's why Insilico Medicine CEO Alex Zhavoronkov said his latest list of venture capitalists (VCs) are so crucial. They give an enormous amount of credibility to the new technology his company is developing . Finding the biological origin of a disease--the potential target for intervention--is the first step in discovering medicines to combat that disease.

Cardiovascular diseases are the most common cause of death in the world. More than a third of deaths in Finland are caused by cardiovascular diseases. The current objective is to create an assessment, based on health data, of each person's risk of illness before they consult a doctor. Andrea Ganna, Group Leader from Institute for Molecular Medicine Finland FIMM at the University of Helsinki and instructor from Harvard Medical School, wants to establish a nationwide, personalised risk assessment as foundation for planning public health interventions. The assessment is based on the health, demographic and genetic information of the citizens.

François-Marie Arouet, 18th century French author and iconoclast, better known as Voltaire, quipped, "The art of medicine consists of amusing the patient while nature cures the disease." Though medicine has progressed in the intervening centuries it remains an art informed by science: both the art of bearing witness, of helping people find meaning in the maelstrom of life's immediate and existential challenges and the less appreciated art of managing uncertainty. It is in this latter regard that AI, broadly speaking, holds promise. Precision medicine, optimised systems and proactive population health have all been forecast but arguably more important is the potential liberation of the time and ingenuity of clinicians to do what they are uniquely able to do - to care for other people. Humans doing what humans do best.