This chapter is interested in the epistemology of algorithms. As I intend to approach the topic, this is an issue about epistemic justification. Current approaches to justification emphasize the transparency of algorithms, which entails elucidating their internal mechanisms -- such as functions and variables -- and demonstrating how (or that) these produce outputs. Thus, the mode of justification through transparency is contingent on what can be shown about the algorithm and, in this sense, is internal to the algorithm. In contrast, I advocate for an externalist epistemology of algorithms that I term computational reliabilism (CR). While I have previously introduced and examined CR in the field of computer simulations ([42, 53, 4]), this chapter extends this reliabilist epistemology to encompass a broader spectrum of algorithms utilized in various scientific disciplines, with a particular emphasis on machine learning applications. At its core, CR posits that an algorithm's output is justified if it is produced by a reliable algorithm. A reliable algorithm is one that has been specified, coded, used, and maintained utilizing reliability indicators. These reliability indicators stem from formal methods, algorithmic metrics, expert competencies, cultures of research, and other scientific endeavors. The primary aim of this chapter is to delineate the foundations of CR, explicate its operational mechanisms, and outline its potential as an externalist epistemology of algorithms.

In 2021 the application of AI enabled advances in many areas of healthcare. We made significant progress in AI for drug discovery, medical imaging, diagnostics, pathology, and clinical trials. Important peer reviewed papers were published and dozens of partnerships were formed. Big Pharma companies and major tech companies became very active in the space. Record amounts of funding were raised, and a few companies even started human clinical trials. Microsoft and NVIDIA launched two of the world's most powerful supercomputers and Microsoft announced Azure OpenAI Service. In 2022 we expect these technologies to converge across the healthcare spectrum. This article summarizes milestones achieved in 2021. This is the first in a series of progress reports I'm writing on the sector that will be supplemented by industry performance data and metrics compiled in partnership with Alliance for Artificial Intelligence in Healthcare (AAIH) and other top tier resources.

Scientists have used artificial intelligence (AI) to create a drug regime for children with a type of deadly brain cancer, where survival rates have not improved for 50 years. Diffuse intrinsic pontine glioma (DIPG) is a rare and fast-growing type of brain tumour in children. These types of tumours are difficult to remove surgically because they are diffuse, which means they do not have well-defined borders suitable for operations. A quarter of children with DIPG have a mutation in a gene known as ACVR1, but there are currently no treatments approved to target this mutation. In a new study, scientists at the Institute of Cancer Research, London (ICR), and the Royal Marsden NHS Foundation Trust were able to use AI to discover that combining the drug everolimus with another called vandetanib could enhance vandetanib's capacity to pass through the blood-brain barrier in order to treat the cancer.

Scientists have successfully used artificial intelligence to create a new drug regime for children with a deadly form of brain cancer that has not seen survival rates improve for more than half a century. The breakthrough, revealed in the journal Cancer Discovery, is set to usher in an "exciting" new era where AI can be harnessed to invent and develop new treatments for all types of cancer, experts say. "The use of AI promises to have a transformative effect on drug discovery," said Prof Kristian Helin, chief executive of The Institute of Cancer Research (ICR), London, where a team of scientists, doctors and data analysts made the discovery. "In this study, use of AI has identified a drug combination which appears to have promise as a future treatment for some children with incurable brain cancer. It's exciting to think that it could become one of the first examples of a treatment proposed by AI going on to benefit patients."

Pocket-size ultrasound devices that cost 50 times less than the machines in hospitals (and connect to your phone). These are just some of the innovations now transforming medicine at a remarkable pace. No one can predict the future, but it can at least be glimpsed in the dozen inventions and concepts below. Like the people behind them, they stand at the vanguard of health care. Neither exhaustive nor exclusive, the list is, rather, representative of the recasting of public health and medical science likely to come in the 2020s.

A recent study released in Nature Reviews: Neurology found that Alzheimer's disease could be diagnosed faster and efficiently using artificial intelligence (AI). The study, conducted by the University of Sheffield, looks at the use of AI technologies, like machine learning, in healthcare to reduce the workflow and economic effects of traditional methods for detecting neurodegenerative diseases. In their study, the use of machine learning in assessing cognitive function was initiated in conjunction with biotech company BenevolentAI. The Sheffield team, along with BenevolentAI, demonstrated in their findings how machine learning algorithms could be efficient for the detection of the brain regions implicated before the onset of rapid cognitive decline or development of Alzheimer's. "Widespread implementation of AI technologies can help, for example, predict which patients showing mild cognitive impairment will go on to develop Alzheimer's disease, or how severely their motor skills will decline over time," said Laura Ferraiuolo, the study's lead author.

Late one January afternoon, British pharmacologist Peter Richardson ran out of his home office and told his wife, "Got it!" She asked what he was talking about and offered a cup of tea. Richardson explained that he had identified a drug that might help people infected with a new virus spreading in China. Richardson's dash was prompted by a finding from artificial intelligence software developed by his employer, BenevolentAI, a London startup where he is vice president of pharmacology. The company has created a kind of search engine on steroids that combines drug industry data with nuggets gleaned from scientific research papers.

AI technologies are catalysing the initial and most crucial step in the biopharmaceutical value chain. The process of drug discovery has been historically slow, labour-intensive, failure-prone, and costly. Its four main stages, as shown below, typically take around five to six years to attain completion. This is a huge amount of time, especially during crisis situations such as the COVID-19 pandemic and considering the fact that drug research and discovery is only the first step in the biopharmaceutical value chain -- all in all it would take about a decade to finish the entirety of this process. Looking back in the past, discoveries were made mostly due to accidents and unexpected observations, like that of penicillin.

Machine learning can also help accelerate the discovery of drugs to help treat COVID-19. BenevolentAI, a UK AI company and AWS customer, turned its platform toward understanding the body's response to the coronavirus. They launched an investigation using their AI drug discovery platform to identify approved drugs which could potentially inhibit the progression of the novel coronavirus. They used machine learning to help derive contextual relationships between genes, diseases and drugs, leading to the proposal of a small number of drug compounds. In just days, BenevolentAI found that Baricitinib (a drug currently approved for rheumatoid arthritis, owned by Eli Lilly) proved the strongest candidate.

As the COVID-19 pandemic continues to infect people across the world, a technological application already familiar to many in the biotech field is lending a key supporting role in the fight to treat and stop it: artificial intelligence (AI). AI is currently being used by many companies to identify and screen existing drugs that could be repurposed to treat COVID-19, aid clinical trials, sift through trial data, and scour through patient electronic medical records (EMRs). The power of AI in COVID-19 is that it is being used to generate actionable information--some of which would be impossible without AI--much more quickly than before. A simple definition of AI is the ability of a computer to rapidly think and learn. AI utilizes machine learning to analyze large amounts of data.