Artificial intelligence (AI) has been the talk of the town lately, with chatbots like OpenAI's ChatGPT, Google's Bard, and Elon Musk's Grok gaining a lot of traction. However, AI isn't as new as these chatbots; rather, interest in AI came decades ago in 1950 when scientist Alan Turing proposed a test of machine intelligence called The Imitation Game in his paper Computer Machinery and Intelligence.

Can machines think? asks Turing in his paper, offering a Turing Test, where a human interrogator would try to distinguish between a computer and human text response.

Since then, advancements in technology have led to more sophisticated AI systems that have been used across different fields, including healthcare and the understanding and treatment of the most complex human organ, the brain.

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Broadly speaking, AI systems reason, learn, and perform tasks commonly associated with human cognitive functions, such as identifying patterns and interpreting speech by processing massive amounts of data.

AI is basically a set of technologies that enable computers to perform a variety of advanced functions. The backbone of innovation in modern computing, AI encompasses different disciplines, including:

These AI models that simulate cognitive processes and aid in complex cognitive tasks such as language translation and image recognition are based on biological neural networks, which are complex systems of interconnected neurons and help train' machines to make sense of speech, images, and patterns.

The intricate and intelligent human brain has been presenting a challenge for scientists to unlock possibilities for human augmentation. However, while AI has been harnessed to create the likes of Apple's Siri, Amazon's Alexa, and IBM's Watson, the truly transformative impact will only be achieved when artificial neural networks are augmented by human native intelligence, an outcome of centuries of survival.

Although computers still can't match the complete flexibility of humans, there are programs that manage to execute specific tasks, with the scope of AI's applications expanding daily. This technological progress, coupled with advancements in science, has notably led to the utilization of AI in medical diagnosis and treatment.

By analyzing large amounts of patient data from multiple sources to assist healthcare providers, AI helps get a complete picture of a patient's health for a more accurate prediction and make more informed decisions about patient care. This further helps detect potential health problems earlier before they become potentially life-threatening. Moreover, by using AI, healthcare providers can automate routine tasks, allowing them to focus on more complex patient care.

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Groundbreaking research in neuroscience has led to the development of advanced brain imaging techniques, including:

Concurrently, as AI algorithms, particularly in machine learning and deep learning, have become more sophisticated, this has resulted in an intersection of both fields. Such synchronization is enabling scientists to analyze and understand brain data at an unprecedented scale.

The intersection of AI and neuroscience, the field focusing on the nervous system and brain, is particularly evident in the realm of data analysis. Presently, AI empowers scientists and researchers to map brain regions with unprecedented accuracy. This has been made possible by the technological advancements in AI that allow the classification of intricate patterns of brain data and then making correlations. This collaboration has also paved the way for researchers to better comprehend neural pathways.

With the help of AI, medical diagnostics could be made better by improving the prediction accuracy, speed, and efficiency of the diagnostic process. AI-powered brain image studies have found subtle changes in brain structures that make their appearance prior to their clinical symptoms becoming known, which have enormous potential for early detection and intervention, potentially revolutionizing our approach to neurodegenerative disorders.

For instance, late last month, researchers leveraged AI toanalyze specialized brain MRI scansof individuals with attention-deficit/hyperactivity disorder (ADHD). ADHD is a common disorder, with an estimated 5.7 million children and adolescents between the ages of 6 and 17 diagnosed with it in the US.

The disorder that is increasingly becoming prevalent due to the influx of smartphones can have a huge impact on the patient's quality of life, as children with ADHD tend to have trouble paying attention and regulating activity. Here, early diagnosis and intervention are key to managing it, but ADHD, as study co-author Justin Huynh said:

It is extremely difficult to diagnose.

The study used fractional anisotropy (FA) values as input for training a deep-learning AI model to diagnose ADHD in a quantitative, objective diagnostic framework.

As we saw, by feeding massive amounts of datasets related to brain scans and patient histories, algorithms can distinguish subtle markers that may not be possible for humans. This, in turn, increases diagnostic accuracy, resulting in earlier interventions and better patient outcomes.

Studying new brain-imaging technology to understand the secrets of brain science and then linking it with AI to simulate the brain is also a way to close the gap between AI and human intelligence. Already, there have been a lot of advancements in brain-computer interfaces (BCI) by companies like Neuralink. BCI connects the brain directly to external devices, allowing disabled people to control prosthetics and interact with the world just by thought, showcasing their potential for many scientific and practical applications.

This merger of human intelligence and AI ultimately can create superhumans' but needs computing models that integrate visual and natural language processing, just as the human brain does, for comprehensive communication. In this context, virtual assistants can address both simple and complex tasks, but machines need to learn to understand richer contexts for human-like communication skills.

In healthcare, diagnostics involves evaluating medical conditions or diseases by analyzing symptoms, medical history, and test results. Its goal is to make use of tests such as imaging tests, blood tests, etc, to determine the cause of a medical problem and make an accurate diagnosis to provide effective treatment. In addition, diagnostics can be used to monitor the progress of a condition and assess the effectiveness of treatment.

The potential of AI in treatment is pretty compelling. Artificial intelligence can provide an analysis of a person's brain characteristics as well as their medical history, genetics, lifestyle data, and other factors, based on which it can offer personalized medicine. This way, AI promises tailored treatment plans that take into account the unique intricacies of each patient's brain.

By identifying unique, unbiased patterns in data, AI can potentially also discover new biomarkers or intervention methods. AI-based systems are faster and more efficient than manual processes and significantly reduce human errors.

A team of researchers recently used AI to predictthe optimal method for synthesizing drug molecules. This method, according to the paper's lead author David Nippa, has the potential to reduce the number of required lab experiments significantly, as a result, increasing both the efficiency and sustainability of chemical synthesis.

The AI model was trained on data from trustworthy scientific works and experiments from an automated lab and can successfully predict the position of borylation for any molecule and provide the optimal conditions for the chemical transformation. Already being used to identify positions in existing active ingredients where additional active groups can be introduced, this model will help in developing new and more effective variants of known drug active ingredients more quickly.

Now, let's take a look at some of the publicly traded companies in the medical sector that are making use of the technology.

This pharma giant has been investing in AI for biomedical data analysis and drug discovery and development. With a market cap of $223.48 bln, Novartis stocks are currently trading at $98.27, up 8.17% this year. The company's revenue trailing twelve months (TTM) has been $47.88 bln while having EPS (TTM) of 3.59, P/E (TTM) of 27.30, and ROE (TTM) of 14.94%. Meanwhile, the dividend yield has been 3.57%.

The company has been integrating AI across its operations, including analyzing vast datasets covering public health records, prescription data, internal data, and medical insurance claims to identify potential trial patients to optimize clinical trial design. Using the AI tool has made enrolling patients in trials faster, cheaper, and more efficient, according to Novartis.

This research-based biopharmaceutical company has a market cap of $163.238 bln and its shares are currently trading at $28.97, down 43.58% this year. The company's revenue trailing twelve months (TTM) has been $68.53 bln while having EPS (TTM) of 1.82, P/E (TTM) of 15.88, and ROE (TTM) of 11.05%. Meanwhile, the dividend yield has been 5.67%.

Pfizer has been showing a lot of interest in leveraging AI to enhance its drug discovery efforts. The company has partnered with many AI companies, such as CytoReason, Tempus, Gero, and Truveta. Meanwhile, to improve its oncology clinical trials, Pfizer signed a data-sharing agreement with oncology AI company Vysioneer, which also has an FDA-cleared AI-powered brain tumor auto-contouring solution called VBrain.

In addition to creating an ML research hub to create new predictive models and tools, Pfizer also partnered with one of the largest cloud providers in the Amazon Web Services for using cloud computing in drug discovery and manufacturing. This partnership has been particularly valuable during the COVID-19 pandemic in various aspects of the vaccine's development, from manufacturing to clinical trials.

This biopharmaceutical company has a market cap of $200.8 bln, and its shares are currently trading at $64.86, down 4.44% this year. The company's revenue trailing twelve months (TTM) has been almost $45 bln while having EPS (TTM) of 1.89, P/E (TTM) of 34.29, and ROE (TTM) of 16.30%. Meanwhile, the dividend yield has been 2.22%.

The Anglo-Swedish drugmaker has been investing in AI to analyze complex biological data for drug discovery and has been collaborating with AI companies to enhance their research capabilities. Most recently, AstraZeneca signed a deal worth up to $247 million with AI-based biologics drug discovery company Absci to design an antibody to fight cancer. The biologics firm makes use of generative AI to get optimal drug candidates based on traits such as affinity, manufacturability, and safety, among others.

Last month, AstraZeneca formed a health-technology unit dubbed Evinova to accelerate innovation and bring AI to clinical trials. The company has also gained early access to AI-driven' digital twins' and signed an AI-powered drug discovery pact with Verge Genomics through its rare disease division,Alexion.

This AI-enabled drug discovery and development company has a market cap of $86.45 bln, and its shares are currently trading at $0.545, down 84.43% this year. The company's EPS (TTM) is 0.75, and P/E (TTM) is 0.72.

BenevolentAI is a clinical-stage company that aims to treat atopic dermatitis as well as potential treatments for chronic diseases and cancer. It uses predictive AI algorithms to analyze and extract the needed insights from the available data and scientific literature. Back in May this year, as part of a strategic plan to position itself for a new era in AI, the company shared that it would reduce spending and free up net cash to increase its financial flexibility.

The company has an established partnership with other big pharmaceutical companies such as GSK and Novartis, while its collaboration with AstraZeneca is to develop drugs for fibrosis and chronic kidney disease. A few months ago, BenevolentAI also partnered with Merck KGaA to leverage its expertise in oncology and neuroinflammation and support the company's AI-driven drug discovery plans by focusing on finding viable small molecule candidates.

As we saw, AI has vast potential to enhance the diagnosis and treatment of brain diseases. It can even help predict brain disorders based on minor deviations from normal brain activity, leading to improved patient outcomes and a more efficient and effective healthcare system. However, it must be noted that this intersection of AI and the human brain is not without its ethical concerns and hence demands strict privacy safeguards.

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The Growing Synergy of AI and Neuroscience in Decoding the Human Brain - Securities.io