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Many people who rely on power wheelchairs to get around will soon let onboard AI negotiate obstacles, adjust speeds and avoid collisions. The algorithmic assistance will pair with millimeter-wave radar and continuous camera data for additional functionality.

Thats all thanks to clinical researchers at Northwestern Universitys Feinberg School of Medicine and their commercial collaborators in the assistive technology industry.

The advance is one of four healthcare AI innovations under development at the Chicago institution and described in the winter edition of Northwestern Magazine.

Heres a summary of each.

1. Building a better means of assistive mobility. Joystick controls are effectively out of reach for more than 15% of the 500,000 Americans using power wheelchairs. Its mainly for them that Brenna Argall, PhD, is working with LUCI Mobility Inc. to smarten up power wheelchairs with AI. Make that mainly but not exclusively, says Argall, an engineer and roboticist at Northwesterns McCormick School of Engineering and an associate professor of physical medicine and rehabilitation at Feinberg. In her words:

A lot of this technology would be helpful for any wheelchair user, just as driver-assist technologies on todays cars are helpful even if you already know how to drive. Even for wheelchair users who dont have severe motor impairments, this technology could still increase their access to the world.

2. Reducing stress during pregnancy. Pointing out that, in the absence of concerted support, prenatal anxiety can bring on complications for mother as well as child, the magazine spotlights a Northwestern team led by Feinberg associate professor of preventive medicine Nabil Alshurafa, PhD. The team is combining an AI algorithm with wearables and digital surveys to gauge and alleviate stress in mothers-to-be. Team member Maia Jacobs, PhD, an assistant professor of preventive medicine at Feinberg and of computer science at McCormick, says:

We have the tools to address stress in the moment. This new algorithm gives us a way to not only provide an intervention when a person is in the throes of stress but also to look for ways to reduce stress across the pregnancy.

3. Personalizing precision care for heart patients. Northwestern cardiologist Sanjiv J. Shah, MD, and colleagues are using machine learning to uncover patterns in diagnostic data that are indicative of heart muscle stiffening due to heart failure with preserved ejection fraction (HFpEF). What weve done in HFpEF is applicable to so many medical conditions: diabetes, schizophrenia, depression, hypertension, you name it, says Shah. More:

There are a lot of skeptics of precision medicinethe right treatment for the right patient at the right time. But Im a believer. With the AI technologies we have today, we can identify subtypes within broad constructs of diseases, and that knowledge can be harnessed to create tailored treatments.

4. Improving autism diagnostics. Molly Losh, PhD, professor of learning disabilities and associate dean for research:

Across prosodic speech features, some people with autism show sing-songy patterns while others might be monotone, and others might have completely different speech patterns. Machine learning has the potential to pull out those fine-tuned differences and really help us understand them.

The feature article was reported by Clare Milliken, a senior writer at the magazine. It includes input from Abel Kho, MD, director of Northwesterns Institute for Artificial Intelligence in Medicine. Read the full piece.

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Machine Learning Meets Mathematics: A Symbiotic Evolution

Mathematicians worldwide are increasingly harnessing the power of machine learning tools to untangle intricate mathematical problems. The recent Mathematics and Machine Learning 2023 conference, organized at Caltech by Professor Sergei Gukov, serves as a testament to this burgeoning collaboration between data scientists and mathematicians.

Machine learning, a branch of artificial intelligence, is a whiz at recognizing patterns and analyzing complex issues, making it an invaluable ally in the world of mathematics. Its capable of providing insights into daunting mathematical conundrums like the Riemann hypothesis and the smooth Poincar conjecture. Gukov and his team have been pioneering in this area, applying machine learning to unravel the mysteries of ribbon knotsa property intrinsically linked to the smooth Poincar conjecture.

But the relationship between mathematics and machine learning isnt one-way. Mathematics also brings fresh, innovative ideas to the table in the development of the algorithms that fuel AI tools. This symbiosis was one of the focal points of the Mathematics and Machine Learning 2023 conference, supported by the Richard N. Merkin Center for Pure and Applied Mathematics at Caltech.

Yi Ni, a fellow professor at Caltech, highlights the potential of machine learning to forge new connections within mathematics. However, he also underlines the essential role of mathematicians to properly frame problems for computational examination.

The conference also emphasized the need to shift away from the black box approach to machine learning. Mathematically informed perspectives have the potential to increase transparency and understanding of machine learning algorithms, which could lead to more reliable and interpretable models.

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Machine learning (ML) approaches are enhancing the efficiency and success rate of chemical space exploration in drug discovery, but from our perspective, the most successful programs will also play to human strengths.

Although not a stand-alone drug discovery approach, because it relies on experimentally generated data, ML can augment existing drug discovery approaches, such as high-throughput screening, virtual screening, DNA-encoded chemical libraries and fragment- and structure-based methods. When it is implemented strategically, the results are clear. For example, Verge GenomicsVRG50635 is in phase I clinical trials for the treatment of amyotrophic lateral sclerosis (ALS), and Insilico MedicinesINS018_055 is in Phase II clinical trials for the treatment of idiopathic pulmonary fibrosis. Both were uncovered by AI-driven drug discovery.

But with all the recent excitement around ML, it is important to emphasize that the human factor should not be eliminated from the iterative design of compounds, data curation or benchmarking. A chemist should always have the final say in the prioritization of ML-generated compounds, so that the focus on progressing high-quality chemical matter can be maintained as our computational capabilities evolve. We remain responsible for supplying high-quality data and exercising scientific rigour when drawing conclusions from our experiments.

One inherent advantage of human expertise is our capacity to think critically and consider problems in a broad context. The best ML models might pass aTuring test, but will generally be less accurate in extrapolating beyond the corpus of data on which they were trained. On the other hand, ML models are better suited to comprehend and leverage complex relationships within large datasets holistically than a human brain.

The need for general awareness regarding the distinct advantages humans and machines offer is important, but so is the need for robust processes for both parties to exchange information with each other. The responsibility falls to us in both cases. We must tailor our descriptions of molecules to the ML models to incorporate as much context as possible. We are also responsible for querying the ML models and predictions for relevant information and interpreting it sensibly in our decision making.

With the abundance of data at medicinal chemists fingertips, rational design decisions have become more difficult. The breadth of chemical space we can access is expanding exponentially faster than we can design, make, test, and analyze chemical libraries, so our intuition regarding the opportunity cost of each new library is now less clear than before.

We can mitigate this by leveraging domain knowledge and computational tools, including ML, to inform and prioritize focused library designs according to an objective (e.g. maximizing diversity relative to an existing library deck, or modulating a particular biological target class).

Diversity itself is not a well-defined term and, therefore, difficult to measure, even in a relative sense. The underlying motivation behind a focus on diverse library designs is to cast a wide net into chemical space and then delve into promising chemotypes, which inherently assumes thesimilarity principle. But as with many rules, there are exceptions (activity cliffs are one example). Furthermore, each chemical representation and similarity score has its own merits and limitations, but none are universally reliable in the measurement of similar bioactivity. So, by extension, none are universally reliable in measuring library diversity in a relevant context, either.

The development ofmolecular representations which localize bioactive compounds in chemical space and similarity metrics which are consistent with medicinal chemists intuition are expected to have a positive impact in addressing these concerns.

Using ML, predictions about the safety and efficacy of promising chemical matter can be made at the design stage rather than the analyze stage. The predictive power of the current state of the art is continuously improving, so the value offered by these early-stage predictions continues to increase. Substructure filters can be particularly helpful to weed out molecules that can potentially cause in vivo toxicity, instability, assay interference or synthesis challenges.

It is also important to remember that ML does not replace experimental measurement of endpoints related to safety and efficacy. It could be used to provide estimates beforehand, but the measurements themselves will (and should) always be part of the approval process. If ML is deployed complementarily to other drug discovery approaches, it can discover chemical matter that would not have been found by those other approaches, so we expect it to have a positive impact on the safety and efficacy of future drug candidates overall.

Current innovations are likely to have a profound impact on the discovery of future therapeutics. For example, improved assessment of the synthesizability of ML-generated compounds can enable drug-likeness estimates, retrosynthesis prediction and generative compound design. The latter is not yet as mature as predictive modeling approaches, but we anticipate that its impact will grow over time.

We can also leverage explainable AI approaches to query the ML models for which information they found to be most influential in making their predictions. Peering into the black box helps us understand the models perspective on the underlying chemistry, which can reveal new insights, such as the putative pharmacophore within each molecule. Confidence estimates are also helpful in assessing how sure the model is about each prediction, so we know which ones to take with bigger proverbial grains of salt.

We would like to emphasize a common sentiment in the ML community: the practice of sharing datasets and code for any public communications describing nonproprietary advances is not only encouraged, it is of the utmost importance. The ability to reproduce results is critical to the scientific method, and the transparency and accountability that stems from that facilitates our advancement as a community.

Data validity should also be checked and communicated consistently. Unfortunately, many datasets in the public domain, such as patents, are of low quality. To avoid propagating that pattern, dataset preparation and any processing steps should be documented wherever possible.

The standardization of benchmark datasets for common ML use cases remains crucial as well. These already exist for certain use cases, such as theTherapeutic Data Commons (TDC) forabsorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, and theOpen Reaction Database (ORD) for reactivity prediction. We hope that this trend continues.

Overall, there is a great deal of hype in the scientific community around AI and ML, which should only be perpetuated with appropriate care and diligence. Its an exciting time for the community, and we should celebrate advances where we can. Equally, we should not lose focus on the concrete value offered by AI and ML in the broader context of drug discovery, which is driven by, and for, people.

The task at hand for drug researchers is striking the right balance between AI/ML and human intervention. The most successful drug discovery programs will play to the strengths of both, for the benefit of all.

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Using Machine Learning to Accelerate Drug Discovery - The Medicine Maker

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Dublin, Jan. 10, 2024 (GLOBE NEWSWIRE) -- The "AI Code Tools Market by Offering (Tools (Technology (ML, NLP, Generative AI), Deployment Mode) and Services), Application (Data Science & Machine Learning, Cloud Services & DevOps, Web Development), Vertical and Region - Global Forecast to 2028" report has been added to ResearchAndMarkets.com's offering.

The global AI code tools market is valued at USD 4.3 billion in 2023 and is estimated to reach USD 12.6 billion by 2028, registering a CAGR of 24% during the forecast period. The driving factor behind the rapid adoption and advancement of AI code tools, particularly generative AI coding tools, lies in the transformative impact they have on software development. These tools are ushering in a new era of AI-assisted engineering workflows, enabling developers to code more efficiently and productively. With the emergence of generative AI coding tools, developers receive code suggestions and even entire functions by simply using natural language prompts or working with existing code. These innovations are quickly altering the coding landscape.

The tools segment is projected to hold the largest market size during the forecast period

AI code tools, often referred to as AI code generators, have revolutionized software development by incorporating artificial intelligence and machine learning into the coding process. These tools are designed to assist developers in writing, optimizing, and managing code efficiently. They offer a wide range of functionalities, from code autocompletion and suggestions to automated testing, code review, and even code generation. AI code tools have significantly increased developers' productivity, enabling them to work faster and more effectively. These tools hold the potential to transform the future of software development by making it more accessible, efficient, and error-free.

By Technology, Machine Learning Segment is registered to grow at the highest CAGR during the forecast period

AI code tools, specifically within the domain of machine learning, encompass a dynamic set of technologies, including deep learning, recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. Deep learning techniques delve into the intricate layers of neural networks to extract complex patterns, making them ideal for tasks like image recognition and natural language processing. RNNs and LSTMs, on the other hand, excel in sequential data analysis, allowing for tasks such as time series forecasting and language modeling. These AI code tools in the realm of machine learning form the backbone of intelligent systems, enabling data-driven decision-making and the development of applications that can understand, learn from, and respond to intricate real-world data.

Asia Pacific is projected to witness the highest CAGR during the forecast period

The Asia-Pacific region stands as a dynamic powerhouse of economic strength and technological advancement, poised to contribute a substantial 70% of global growth in 2023, surpassing other regions. As the region holds more than 50% of the world's population, any technological shifts like those being heralded by AI are expected to shape the future of the region. Many Asian countries such as China, India, Japan, and others are leveraging information-intensive AI technologies, with conversational AI being one of the leading technology trends. Countries like China, Japan, South Korea, India, and Singapore are heavily investing in artificial intelligence (AI), positioning the APAC region as the world's fastest-growing AI market. This emphasis on AI offers vast growth potential and innovation opportunities for software companies.

Research Coverage

The market study covers AI code tools across segments. It aims at estimating the market size and the growth potential across different segments, such as offering, application, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.

Companies Profiled

Premium Insights

Market Dynamics

Key Attributes

For more information about this report visit https://www.researchandmarkets.com/r/4f5miu

About ResearchAndMarkets.comResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

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Global AI Code Tools Market Worth $12.6 Billion by 2028: Increasing Demand for Automation & Efficiency in Software ... - GlobeNewswire

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