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Globally, there has been a significant increase in the number of people using prescription drugs for reasons other than why they were prescribed, sometimes combining them with other substances such as alcohol to sleep better or stimulants to perform better. A team of computer scientists and emergency physicians from Emory, Oregon, and Pennsylvania universities in the United States used AI to analyze drug misuse and the emotions users felt during times of use. The study, "Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use," was published in the journal Health Data Science. In 2021, more than 108,000 people in the U.S. died from drug overdoses, a number that is up 20% from 2020, many of these deaths were caused by the ingestion of prescription drugs, often mixed with other substances. In France, more than 10,000 people die each year as a result of medication misuse.

The discovery of new drugs is an undeniably important undertaking and represents a massive global market. Statista indicates that the drug discovery market worldwide finds itself on an exponential trajectory, with the expected market value poised to reach 71 billion U.S. dollars by 2025. As of 2016, the market was valued at just 35.2 billion U.S. dollars. Of course, this comes as no surprise; the U.S. pharmaceutical industry, after all, was coined'Big Pharma' for a reason. By 2021, Big Pharma profits for prescription drugs are expected to reach $610 billion and, in 2015, Americans spent $457 billion on prescription drugs.

In this paper, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD patients, and 2) their prescription drug provided by neurologists. Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug. Finally, for the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model. From the methodology part, our proposed model, namely Prescription viA Learning lAtent Symptoms (PALAS), could recommend prescription using the multi-modality representation of the data. In PALAS, a latent symptom space is learned to better model the relationship between symptoms and prescription drug, as there is a large semantic gap between them. Moreover, we present an efficient alternating optimization method for PALAS. We evaluated our method using the data collected from 136 PD patients at Nanjing Brain Hospital, which can be regarded as a large dataset in PD research community. The experimental results demonstrate the effectiveness and clinical potential of our method in this recommendation task, if compared with other competing methods.

Scanning today's headlines about Artificial Intelligence reveals an atmosphere of optimism tempered by caution. Artificial intelligence presents a huge opportunity for everyone in the value chain: health providers and organizations, vendors, regulatory agencies, and, perhaps most importantly, patients. It's driving stats like these: Sixty-two percent of respondents in a 2019 survey by OptumIQ report "having implemented an AI strategy--an increase of nearly 88% from 2018 (33%)--while 22% report being at late stages of implementation." But in these early days, the way forward can be unclear, muddied by too many choices, too many voices, and too much-sunk cost in legacy systems and thinking. To gauge how industry leaders are using or planning to deploy AI, and to collect the best thinking on the most urgent opportunities for AI in healthcare in the near term, we asked experts and influencers to weigh in.

Developing prescription drugs is a high-cost, high-risk endeavor. Average research and development for an approved prescription drug requires an investment of $2.9 billion and takes more than 11 years. Clinical trials alone can cost an average of $1.1 billion over 6.6 years. In fact, clinical trials account for a staggering 40 percent of the pharmaceutical industry's research budget. To make matters worse, only 14 percent of drugs that enter clinical trials are eventually approved.

My nose is pressed to the glass as I watch thousands of human cellular samples whisk by, running through a large, elaborate machine. There are lab workers bustling around in neatly pressed lab coats, jotting down notes on their clipboards. It looks like a scene from a futuristic movie. This is real life, and the technology in use is poised to change the world of healthcare as we know it. "I never wanted to work for a pharmaceutical company," says Amanda Guisbond, the director of corporate communications at Recursion Pharmaceuticals.

From apocalyptic prognostications to impassioned positions -- and everything in between -- it seems like everyone and their mother has developed an opinion on the role artificial intelligence (AI) will play in shaping society in the coming decades. The disparity between each of these notwithstanding, what's clear is that "narrow AI" is already starting to have an impact on everything from software development to education to insurance. Despite multiple dalliances with AI stretching all the way back to the 1970s, my industry, healthcare, has yet to embrace AI with the same vigor as many others. Fortunately, this is finally starting to change. Consulting firm Frost & Sullivan reports that the healthcare AI market is set to experience a compound annual growth rate of 40 percent through 2021, largely because AI has the potential to improve health care outcomes by 30 to 40 percent while simultaneously cutting the costs of treatment in half.

People who need medication rarely take just one pill. Many of those who have been prescribed medications for health reasons take upwards of five medications per day. According to the U.S. Centers for Disease Control and Prevention, the percentage of persons using at least one prescription drug in the past 30 days, in the U.S., is 48.9 percent; and the percentage of people using three or more prescription drugs ('polypharmacy') in the past 30 days stands at 23.1 percent; whereas those using five or more prescription drugs in the past 30 days is 11.9 percent. The figures for the U.S. will mirror those of many other high-income countries. From the ubiquitous aspirin to the most sophisticated prescription medicine on the market, all medications come with side effects.

Patients are increasingly using wearable technology and mobile apps to track and monitor their health, creating interesting implications for the healthcare supply chain. The tech reduces pressure on doctors, hospitals and pharmacies to provide certain necessary services to the patient (an exception -- prescription drugs), but it also allows insurers to glean more accurate health information from the insured. According to the study, which surveyed 2,301 consumers, "The percentage of consumers willing to share with their insurance carrier personal data collected from their wearable devices has increased over the past year, from 63% in 2016 to 72% today." Additionally, "One in five respondents (19%) said they have already used AI-powered healthcare services, and most said they are likely to use AI-enabled clinical services, such as home-based diagnostics (cited by 66% of respondents), virtual health assistants (61%) and virtual nurses that monitor health conditions, medications and vital signs at home (55%)." That could lead to a winnowing of the healthcare industry; as patients rely more and more on technology to deliver certain health services, doctors may evolve to provide only more advanced care, especially if retailers move deeper into the pharmacy and healthcare business. The tech also cuts out the industry middleman, reducing the need for lower-ranking medical professionals whose services are now absorbed by various apps and wearables.