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UMaine researchers examine issues around using AI in family therapy – UMaine News
UMaine researchers examine issues around using AI in family therapy
A new paper from two University of Maine researchers explores the challenges and opportunities for scholars and practitioners when it comes to using AI to study and develop interventions for relationship and family therapy.
“Challenges and opportunities in using interpretable AI to develop relationship interventions” was published in Family Relations, the academic research journal of the National Council on Family Relations, as part of a special issue on AI in family life.
The use of AI in therapy is still in its infancy but has potential to provide families and couples with personalized support to strengthen bonds and overcome relationship problems, according to Daniel Puhlman, assistant professor of family studies in the UMaine College of Education and Human Development and the article’s lead author.
“Couples going through a separation, for example, where you have high emotions and high conflict, just being in the same space can be difficult, if not dangerous,” Puhlman said. “In a situation like that, AI’s ability to be interpretive and suggest therapeutic interventions or treatment measures could be a very powerful tool.”
Puhlman notes that as more people become familiar with them, AI technologies are already integrating into family life and other types of relationships. However, he and his co-author, assistant professor of computer science Chaofan Chen, note that AI itself is a broad term that encapsulates several different technological systems and processes where machines are programmed to mimic human cognition and perform tasks that require human intelligence.
“The specific sub-area of AI that we found most relevant to addressing family science problems is machine learning, which uses algorithms that allow computers to learn from datasets and make predictions or decisions based on that data,” Puhlman said.
Strategies that incorporate human expertise and feedback — known as the human-in-the-loop technique — are important for improving the accuracy of machine learning models, he adds.
“It’s especially important in fields like family and relationship science, health care and law, where human judgment is critical,” said Puhlman.
According to the researchers, most of the AI-based technologies currently used in therapeutic contexts offer support to individuals rather than to couples or families. The tools that are most widely used are largely educational and not used for actual treatment or interventions — therapists can use AI to summarize session notes, for example. Part of the challenge is that while AI is good at interpreting and reporting data when it has a strict structure in place, human behavior is complicated.
“Just think about why human beings do the things we do, say the things we say, think the things we think, or how we interact with the world around us. Then put two or more of these complex, messy, dynamic creatures together, and you can see the challenges for a system that relies on a strict structure,” Puhlman said.
Another challenge is that there are several different ways to practice therapy, and what works in one situation may not apply to another.
“You can train AI to use a therapeutic mode, but training it to know when it is appropriate to use and why to use it versus a different method gets quite complex,” Puhlman said.
The authors explore these challenges, as well as ethical concerns about AI and privacy; AI providing inaccurate information; and AI and bias.
They also highlight opportunities in four specific areas to further develop AI in the context of relationship therapy: diagnosing relationship problems, providing autonomous treatment to clients, predicting successful treatment outcomes and using biomarkers to monitor client reactions.
“Already we’ve seen some researchers using machine learning to study parenting relationships or diagnose relationship problems. Chatbots could provide treatment to underserved populations or help therapists maximize positive outcomes for their clients. All of the challenges we identified still need to be addressed, but these technologies have the potential to make a real difference,” Puhlman said.
Contact: Casey Kelly, casey.kelly@maine.edu
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Ciena Powers SingAREN to Enhance AI-Driven Research with High-Speed Network
For over a decade, Singapore has consistently ranked highly on the Global Innovation Index, an annual ranking of 130 economies. In 2024 it achieved its highest position yet – 4th globally.
This strong performance is largely due to steady, long-term investment in research & development (R&D) as a key pillar of Singapore’s economic development strategy.
Supporting Singapore’s Research, Innovation and Enterprise (RIE) ecosystem is the Singapore Advanced Research and Education Network (SingAREN), established in 1997. SingAREN is the sole provider of dedicated local and international network services for the local Research and Education community.
SingAREN’s network supports the SingAREN Open Exchange (SOE) for high-speed research and education connectivity, eduroam, an international Wi-Fi internet access roaming service for the international research and education community, and FileSender SG as a platform for large file transfers, among other services running on its network.
RIE is vital to Singapore’s progress, fostering economic growth and competitiveness. It also drives scientific advancements that can potentially address societal challenges and enhance our well-being.
SingAREN has supported robotic telesurgery trials across international boundaries, which require precise, instantaneous control, and a low-latency network for real-time collaboration.
SingAREN also enables high-speed, resilient connectivity to the National Supercomputing Center (NSCC), which manages Singapore’s national high-performance computing (HPC) resources, supporting research and innovation across various fields. In particular, the NSCC’s expertise and specialized infrastructure are often leveraged to manage and analyze genomic data. Transferring genomic data is typically difficult due to its massive data size.
SingAREN provided a high-speed link to the Cancer Science Institute of Singapore for a research project, transmitting more than 2 petabytes of cancer genomics data downloaded from repositories in the United States into NSCC. The research involved harmonizing petabytes of whole genome sequencing data, and downloads were expected to be extremely fast, stable, and efficient, after which, the downloaded data would be analyzed and reprocessed with high computing power.
This is but one of the examples of collaboration with NSCC to transfer, download, analyze and process genomic data.
Academic research is experiencing explosive growth and requires more data than ever before, fuelled by AI and Machine Learning (ML), and cloud computing. The increasing use of generative and agentic AI will also impact SingAREN and its research partners significantly, leading to increased data volume. This type of advanced research activity will not be possible without a robust, scalable, low-latency network.
In the coming months, SingAREN will enhance its network to further support its research institution partners. These plans include the SingAREN Lightwave Internet Exchange (SLIX) 2.5 project, to provide high-speed, secure connectivity by 2027, and the SLIX 3.0 vision to build a future-ready network that incorporates quantum-safe networking, AI research, and haptic surgery. SingAREN also aims to expand cybersecurity threat intelligence sharing and continue infrastructure upgrades, such as implementing 400G switches and enhancing Points of Presence (PoP) resilience.
SingAREN uses Ciena’s 6500 powered by Ciena’s WaveLogic programmable coherent optic technology. Deployed by Ciena partner, Terrabit Networks, Ciena’s 6500 supports SingAREN to respond to changing requirements on-demand, allowing the REN to continually maximize network efficiencies and offer customizable service delivery over any distance.
Associate Professor Francis Lee, Vice President of SingAREN
Our backbone network, powered by Ciena’s 6500 optical solution, is built to handle the growing demands of AI, genomics, and big data applications—transmitting petabytes of data. To support the advancement of Singapore’s Research, Innovation and Enterprise agenda, our flexible, low-latency network can now seamlessly deliver 10G to 100G connections to member institutions. We continue to push the boundaries of research and innovation, ensuring connectivity is never a limiting factor.
AI Research
SoundHound AI Stock Sank Today — Is the Artificial Intelligence Company a Buy?
SoundHound AI (SOUN -4.73%) stock saw a pullback in Thursday’s trading. The company’s share price fell 4.7% in the session and had been down as much as 8.1% earlier in trading.
While there doesn’t appear to have been any major business-specific news behind the pullback, investors may have moved to take profits after a pop for the company’s share price earlier in the week. Despite today’s pullback, the stock is still up roughly 9% over the last week of trading. Even more striking, the company’s share price is up roughly 39% over the last three months.
Image source: Getty Images.
Is SoundHound AI stock a good buy right now?
SoundHound AI has been highly volatile over the last year of trading. While the company’s share price is still up roughly 197% across the stretch, it’s also still down approximatley 49% from its peak in the period.
Even as the company’s sales base has ramped up rapidly, sales growth has continued to accelerate. Revenue increased 151% year over year in the first quarter of the company’s current fiscal year, which ended March 31. The company still only posted $29.1 million in sales in the period, but sales growth in the quarter marked a dramatic improvement over the 73% annual growth it posted in the prior-year period.
SoundHound is an early mover in the voice-based agentic artificial intelligence (AI) space, and it has huge expansion potential over the long term — but its valuation profile still comes with a risk. The company now has a market capitalization of roughly $4.9 billion and is valued at approximately 31 times this year’s expected sales.
For investors with a very high risk tolerance, SoundHound AI could still be a worthwhile investment. The company has been posting very impressive sales momentum, but its valuation already prices in a lot of strong growth in the future. If you’re looking to build a position in SoundHound AI stock, using a dollar-cost-averaging strategy for your purchases may be better than buying in all at once at today’s prices.
Keith Noonan has no position in any of the stocks mentioned. The Motley Fool has no position in any of the stocks mentioned. The Motley Fool has a disclosure policy.
AI Research
Artificial Intelligence (AI) in Healthcare Market worth
The prominent players operating in the Artificial Intelligence (AI) in healthcare market include Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US)
Browse 902 market data Tables and 67 Figures spread through 711 Pages and in-depth TOC on “Artificial Intelligence (AI) in Healthcare Market by Offering (Integrated), Function (Diagnosis, Genomic, Precision Medicine, Radiation, Immunotherapy, Pharmacy, Supply Chain), Application (Clinical), End User (Hospitals), Region – Global Forecast to 2030
The global Artificial Intelligence (AI) in Healthcare Market [https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html?utm_source=abnewswire.com&utm_medium=paidpr&utm_campaign=artificialintelligenceinhealthcaremarket], valued at US$14.92 billion in 2024, is forecasted to grow at a robust CAGR of 38.6%, reaching US$21.66 billion in 2025 and an impressive US$110.61billion by 2030. The growing incidence of chronic diseases, linked with an increasing geriatric population, puts substantial financial pressure on healthcare providers. There is a rising need for the early detection of conditions such as dementia and cardiovascular disorders. This can be done by analysing imaging data to recognize patterns, which helps create personalized treatment plans.
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Browse in-depth TOC on “Artificial Intelligence (AI) in Healthcare Market”
882 – Tables
61 – Figures
738 – Pages
By tools, the Artificial Intelligence (AI) in healthcare market for machine learning has been bifurcated into deep learning, supervised learning, reinforcement learning, unsupervised learning, and other machine learning technologies. The deep learning segment accounted for the largest share of the Artificial Intelligence (AI) in healthcare market in 2024. The capability to process vast amounts of unstructured medical data, such as electronic health records (HER), imaging, and genomics, allows accurate disease diagnosis and prediction. The integration of deep learning into healthcare is significantly boosting the AI in healthcare market, leading to substantial investments in diagnostic tools and predictive analytics. As computational power and data availability continue to increase, deep learning is set to unlock further advancements, solidifying its position as a key enabler of next-generation healthcare technologies.
By end user, the AI in healthcare market is segmented into healthcare providers, healthcare payers, patients, and other end users. In 2024, healthcare providers accounted for the largest share of the AI in healthcare market. The large share of this end-user segment can be attributed to the increasing budgets of hospitals to improve the quality of care provided and reduce the cost of care.
By geography, the Artificial Intelligence (AI) in healthcare market is segmented into five main regions: North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. The Asia Pacific region is projected to see a substantial growth rate during the forecast period. The Asia Pacific (APAC) region is experiencing substantial growth in adopting AI technologies within the healthcare sector, driven by a combination of demographic shifts, technological advancements, and increased investments in innovation. The rising elderly population in the region is a key factor, with the proportion of individuals aged 65 years and above increasing significantly. The demand for advanced healthcare solutions has surged as the aging population faces chronic and age-related conditions, necessitating efficient diagnostic, monitoring, and treatment tools. AI technologies are being integrated into various healthcare applications, including predictive analytics, telemedicine, medical imaging, and patient management systems. These innovations aim to address gaps in healthcare access, improve diagnostic accuracy, and streamline operations across the region.
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The prominent players operating in the Artificial Intelligence (AI) in healthcare market include Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US), GE Healthcare (US), Medtronic (US), Oracle (US), Veradigm LLC (US), Merative (IBM) (US), Google (US), Cognizant (US), Johnson & Johnson (US), Amazon Web Services, Inc. (US), among others. These companies adopted strategies such as product launches, product updates, expansions, partnerships, collaborations, mergers, and acquisitions to strengthen their market presence in the Artificial Intelligence (AI) in healthcare market.
Koninklijke Philips N.V. (Netherlands)
Koninklijke Philips N.V. is a leading player in the AI in the healthcare market. The company utilizes AI to deliver innovative tools across various areas, including diagnostic imaging, patient monitoring, and precision medicine. Its advanced AI-driven platforms, such as the Philips HealthSuite, facilitate the integration and analysis of extensive clinical data, which supports personalized treatment plans and improves patient outcomes. Philips focuses on organic and inorganic growth strategies to expand its market presence.
Strategic partnerships in high-potential markets and collaborations have been the key growth strategies of the company over the years. For example, in February 2025, Philips partnered with Medtronic to educate and train cardiologists and radiologists in India on advanced imaging techniques for structural heart diseases. This partnership aims to upskill 300+ clinicians in multi-modality imaging such as echocardiography (echo) and Magnetic Resonance Imaging (MRI), especially for End-Stage Renal Disease (ESRD) patients. In November 2023, Philips and NYU Langone Health partnered to focus on patient safety and outcomes. This partnership integrated innovative health technologies, including digital pathology, clinical informatics, and AI-enabled diagnostics, enabling real-time collaboration among clinicians. The company also focuses on winning contracts across several companies in the healthcare space. This helps the company expand its footprint. For instance, in September 2022, Philips and Mandaya Royal Hospital Puri (MRHP) in Jakarta underwent a digital transformation in a strategic partnership, enhancing patient-centered care and healthcare services.
Microsoft Corporation (US):
Microsoft Corporation is one of the leading providers of software & tools that include advanced AI capabilities in healthcare to improve patient outcomes, streamline operations, and drive innovation. Its Azure-based AI solutions support distinct applications such as medical imaging, genomics, and precision medicine. The company also provides healthcare-specific AI models through its Azure AI Model Catalog, which is constructed to support hospitals and research institutions in building and deploying tailored AI solutions proficiently. Moreover, the integration of Nuance’s AI-powered clinical and diagnostic tools encourages its capacity to support healthcare providers in decision-making and care delivery. The company continuously brings AI capabilities to the platforms in large-scale customer models. For instance, in March 2025, the company launched Microsoft Dragon Copilot, the first unified voice AI assistant in the healthcare industry that enables clinicians to streamline clinical documentation, surface information, and automate tasks.
Microsoft Corporation has invested significantly in R&D, which has improved its product portfolio and position in the AI market. Machine Learning (ML), deep learning, Natural Language Processing (NLP), and speech processing are the key focus areas of the company in the AI in healthcare market. The company continuously invests in a series of services and computational biology projects, including research support tools for next-generation precision healthcare, genomics, immunomics, CRISPR, and cellular and molecular biologics. It has a strong global presence, with key operations supported through its Azure cloud infrastructure across regions like North America, Europe, Asia-Pacific, and the Middle East.
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