How GenAI and Chatbots are Revolutionizing Patient Care?

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TheGen.AI News

How GenAI and Chatbots are Revolutionizing Patient Care?

The healthcare industry is undergoing a significant digital transformation, with generative AI and chatbots playing a key role in patient engagement. Technologies like online symptom checkers, appointment scheduling, patient navigation tools, medical search engines, and patient portal messaging are all important use cases for GenAI and chatbots. These AI-driven tools have the potential to ease the workload on staff while enhancing the overall patient care experience, according to some experts.

However, GenAI in patient-facing applications is not without challenges, including the risks of generating medical misinformation or biased algorithms. As healthcare professionals explore these AI-driven tools, they must also consider safeguards to prevent misinformation and avoid exacerbating health disparities.

Online Symptom Checkers:

Online symptom checkers allow patients to enter their symptoms and receive a list of possible diagnoses, helping them decide whether to seek care or manage their symptoms at home. These tools can improve patient experiences and efficiency by directing patients to the right care setting, such as urgent care or the emergency department, while reducing unnecessary visits to healthcare providers.

While symptom checkers hold promise, their diagnostic accuracy is mixed. A 2022 literature review found that diagnostic accuracy for online symptom checkers ranged between 19% and 37.9%, though triage accuracy was higher, ranging from 48.9% to 90%. Patient reactions to these tools have been lukewarm, especially during the COVID-19 pandemic, when symptom checkers were widely used. Patients were more likely to accept these tools when they seemed competent and human-like, but they preferred them only when they matched the quality of human interactions.

Additionally, these tools could worsen health disparities, as they tend to be used more by younger, female patients with higher digital health literacy. To make online symptom checkers accessible to a wider audience, AI developers must incorporate features like multilingual support, human-like interactions, and easy access to human assistance when needed.

Self-Scheduling and Patient Navigation:

GenAI and conversational AI are showing promise in handling routine patient queries, freeing up healthcare staff to focus on clinical tasks. Chatbots integrated into appointment scheduling systems can help patients schedule visits while answering questions about parking or directions to specific departments.

A December 2023 literature review highlighted the potential of conversational AI to improve the appointment scheduling and patient navigation experience. AI-optimized scheduling can reduce the burden on providers, enhance patient satisfaction, and make healthcare more efficient. However, organizations must consider factors like health equity, broadband access, and patient trust when implementing these technologies.

While challenges remain, AI can be an effective solution for managing routine tasks like booking appointments or providing directions, allowing healthcare staff to focus on more complex issues, such as insurance questions.

GenAI Spurs Surge in Data Center Investments and Energy Demand

Generative artificial intelligence (GenAI) has driven a substantial rise in data center capital expenditures (capex) in the first half of this year, creating increasing strain on the market's energy needs. The Dell’Oro Group reported a 38% surge in global data center capex during the first half of 2024 compared to 2023, attributing this to investments in accelerated servers that support GenAI applications. These include servers equipped with Nvidia Hopper GPUs and custom accelerators like Google’s TPU and Amazon’s Trainium, which have gained traction among hyperscale cloud providers.

This trend aligns with prior reports indicating that hyperscalers are significantly ramping up investments in data centers. According to Synergy Research Group (SRG), hyperscalers now operate over 1,000 large data centers worldwide, accounting for 41% of global data center capacity. Around half of this capacity comes from self-built, owned facilities, while the remainder is sourced from leased data centers. SRG’s chief analyst John Dinsdale also observed that enterprises are increasingly using colocation facilities, reducing their reliance on on-premises data centers—a trend that’s expected to continue as AI services grow in demand.

Dinsdale noted that the rise of GenAI will accelerate this shift, as hyperscalers are better equipped to handle AI operations than most enterprises. Research firm ISG also highlighted that large enterprises plan to nearly double their AI-enabled applications by the end of 2024, rising from an average of 250 applications in 2023 to 488.

Dell’Oro Group projects that full-year data center capex will increase by 35% compared to last year, reaching $400 billion, with Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) leading the charge. Microsoft CFO Amy Hood, during an earnings call, confirmed that the company is constrained by AI capacity and is working with third parties to meet Azure AI demand. Amazon CEO Andy Jassy echoed this, acknowledging the importance of having sufficient capacity to handle AI-driven data center traffic but cautioning against overinvestment, which could hurt returns.

The rapid expansion of AI infrastructure is also raising concerns about energy consumption. MTN Consulting reported that webscale energy use has doubled since 2019, growing at 15% annually over the past two years. Despite the expectation that scale should bring energy efficiencies, power consumption per revenue unit has actually increased since 2021.

However, some experts argue that hyperscaler infrastructure is more energy-efficient than traditional enterprise data centers. John Mennel of Deloitte Consulting noted that cloud-based hyperscaler setups can produce up to 60% fewer emissions than owned data centers, thanks to large investments in sustainability.

While energy consumption remains a concern, it is unlikely to slow data center investments. MTN Consulting’s Matt Walker explained that the race to develop new AI models is driving a competition for skilled developers, tools, energy capacity, and land, with early leaders expected to maintain their advantage for years.

Cloud Adoption Shaped by GenAI Initiatives, Reveals HCLTech Report

Generative artificial intelligence (GenAI) initiatives are driving cloud adoption and increasing the focus on cloud-native practices for application modernization, with 98% of surveyed enterprises exploring customized solutions, according to an HCLTech report. The report, titled 'Cloud Evolution: Mandate to Modernize,' emphasizes that application modernization is crucial for realizing cloud value. Around 80% of enterprises believe that the full potential of the public cloud can only be unlocked when applications are modernized during migration, rather than simply being lifted and shifted.

"Cloud is more than just a technological investment; it is central to how businesses modernize. The integration of cloud, GenAI, and cloud-native practices is vital to enterprise operations, enabling faster decision-making, improved customer experiences, and greater competitive edge," said Siki Giunta, HCLTech’s executive vice president and head of the cloud native center of excellence.

Giunta also highlighted that adopting cloud-native architectures and platform engineering is key for enterprises looking to build a resilient and future-ready IT environment that fosters continuous innovation. By refactoring applications and utilizing cloud-native disciplines, businesses can achieve new levels of agility, scalability, and resilience.

According to the report, 55% of enterprises noted that data for custom GenAI solutions remains on-premises, prompting organizations to initially run some workloads locally.

Based on a survey of 500 senior business and technology leaders across various industries and regions, the report by HCLTech, conducted by global analyst firm ESG, examines how enterprises are evolving their cloud strategies to promote innovation and transformation.

The report also reveals that hybrid, multicloud environments will remain prevalent, with enterprises now 2.2 times more likely to have a deliberate multicloud strategy compared to three years ago. Although public cloud adoption is increasing, 81% of enterprises plan to maintain substantial on-premises infrastructure due to data security and regulatory concerns, underscoring the need for a hybrid cloud approach.

TheOpensource.AI News

Open-Source AI Tool Takes on Google’s NotebookLM, Built in Just One Day

Gabriel Chua, a data scientist at Singapore's GovTech agency, has developed an open-source alternative to Google's increasingly popular NotebookLM. Named "Open NotebookLM," Chua created the tool in a single afternoon using publicly available AI models. Like Google's product, Open NotebookLM transforms PDF documents into personalized podcasts, but with the key difference that it is entirely open-source and free to use.

The tool utilizes Meta's Llama 3.1 405B language model, hosted on Fireworks AI, along with MeloTTS for voice synthesis. Its user-friendly interface, built using Gradio and hosted on Hugging Face Spaces, makes it accessible to users with little technical expertise. Chua's swift development of Open NotebookLM showcases the growing power of open-source AI tools, proving that individual developers or small teams can now create complex AI applications in just hours, a feat previously limited to major tech companies.

However, while Open NotebookLM’s rapid creation is impressive, it raises concerns about the quality and reliability of AI tools developed so quickly. Unlike commercial products, Open NotebookLM may not undergo the same level of rigorous testing and refinement, and users should be cautious, especially when working with sensitive or confidential data. Google's NotebookLM, by comparison, retains several advantages, including integration with the broader Google ecosystem and features like fact-checking and study guide generation, supported by Google’s vast computational resources and proprietary AI models.

The launch of Open NotebookLM marks a significant shift in the AI landscape, illustrating how the barriers to developing sophisticated AI tools are lowering, fostering more competition and innovation. This trend could lead to faster advancements in AI technology but also raises concerns about data privacy, security, and ethical use, as more developers gain access to create powerful AI tools. While open-source projects like Open NotebookLM allow for community-driven improvements, they also present risks if exploited for malicious purposes.

For enterprises and decision-makers, open-source AI tools like Open NotebookLM present both opportunities and challenges. These tools offer cost-effective, customizable alternatives to proprietary software but may lack the support, security, and continuous updates that commercial solutions provide.

As the divide between proprietary and open-source AI blurs, the development of sophisticated AI tools is no longer restricted to large tech companies, potentially creating a more diverse AI ecosystem. This shift highlights the need for strong frameworks to ensure responsible AI development and use. Chua’s work and the broader open-source community are pushing the boundaries of AI development, potentially prompting more collaboration between open-source and proprietary efforts in the future.

Open-Source AI Models Poised to Expand Business Access to Advanced Tools

Open-source artificial intelligence (AI) models are quickly closing the gap with proprietary systems, a shift that could significantly alter the business landscape by making powerful, cost-effective AI tools available to companies of all sizes.

The AI industry is undergoing a transformation as open-source models advance in comparison to proprietary ones. The Allen Institute for Artificial Intelligence (AI2) recently introduced its Molmo family of multimodal models, marking a key step in this trend.

Molmo is AI2’s most advanced open-source AI effort so far. It can handle and generate various types of data, such as text and images, making it applicable across a wide range of industries. However, building competitive open-source AI models presents several challenges.

“Training AI is extremely costly, and maintaining open-source models while covering expenses is a tough task,” said Simona Vasytė, CEO of Perfection42, in an interview with PYMNTS.

Another hurdle is data quality.

“Open-source developers can’t train their models on billions of data points,” Vasytė explained. “For example, Molmo used only 600,000 data points, a small fraction of what OpenAI used.”

This gap in data access can affect the performance of open-source models when compared to proprietary ones.

Democratizing AI: The Open-Source Movement

The development of Molmo and similar projects is part of a broader movement to democratize AI technology. These advanced models are making capabilities that were once limited to large tech firms accessible to a broader range of businesses.

The open-source AI ecosystem has seen a rise in new models, with notable examples including Meta’s LLaMA 2 and Stability AI’s Stable Diffusion XL. This increase in open-source options has sped up innovation and expanded access to advanced AI tools, challenging the dominance of proprietary models from companies like OpenAI and Google.

Vasytė emphasized the importance of open-source AI for driving innovation.

“Open-source alternatives are crucial for accessibility and the healthy development of AI systems,” she said. “Without them, only those with the biggest budgets would have access to the most powerful solutions and the competitive edge that comes with them.”

This shift has important implications for businesses, as open-source AI models could level the playing field, allowing smaller companies to compete more effectively in AI-driven innovation.

“Open-source models give everyone access to AI solutions, enabling businesses to build their own without paying huge fees to big tech companies,” Vasytė added.

Michael Berthold, CEO of KNIME, also pointed out the transparency that open-source models offer.

“In data science, open-source software allows users to understand how a system processes data and produces results,” Berthold said. “With many GenAI tools, we don’t know how they are trained, optimized, or the accuracy of performance claims. Open-source models provide the transparency needed to validate these processes.”

However, open-source AI models come with challenges.

Tony Baker, chief product safety and security officer at Rockwell Automation, highlighted concerns around open-source software (OSS), particularly in critical infrastructure, in a discussion with PYMNTS.

“While OSS has supported digital transformation, additional effort and investment are needed to sustain it,” Baker said, adding that both consumers and downstream users of OSS must adopt unique risk management strategies to fully leverage it.

The Future of Open-Source AI

Industries such as eCommerce and healthcare may discover new ways to utilize these technologies to enhance their operations, products, and services. Vasytė stressed that high-quality data is key to building accurate, high-performing models.

The growing capabilities of open-source AI models offer both opportunities and strategic considerations for businesses. Organizations must carefully weigh whether to invest in proprietary AI solutions or explore open-source alternatives, as this decision could impact their competitiveness and innovation potential.

Looking ahead, Vasytė predicted that open-source models will help keep Big Tech companies in check. As long as developers have access to open-source models, companies like OpenAI and Google will be compelled to remain competitive—not only with their models but also with their pricing.

TheClosedsource.AI News

SoftBank Eyes $500 Million Investment in OpenAI as Part of $6.5 Billion Raise

SoftBank Group Corp.’s Vision Fund is preparing to invest $500 million in OpenAI as part of a larger fundraising effort, according to a report from The Information on September 30, which cited a source familiar with the deal.

Bloomberg previously reported that OpenAI is in talks to raise $6.5 billion from various investors, with a valuation of $150 billion. Thrive Capital is expected to lead this funding round, with Microsoft Corp., OpenAI’s largest investor, also participating. This significant investment would further cement OpenAI’s status as one of the most valuable startups globally. However, this comes during a time of uncertainty for the company, as its Chief Technology Officer, Mira Murati, recently announced her departure, following other leadership changes earlier this year. Additionally, OpenAI is reportedly considering restructuring as a for-profit entity, according to Bloomberg.

While this would be SoftBank’s first investment in OpenAI, the Vision Fund previously backed one of its competitors. In June, SoftBank invested in AI search startup Perplexity AI, valuing the company at $3 billion.

Meanwhile, on September 23, OpenAI announced that one of its official accounts on the social media platform X (formerly known as Twitter) had been compromised. The company confirmed that its @OpenAINewsroom account was hacked, with the attacker posting messages promoting links to a cryptocurrency token falsely linked to the startup.

Additionally, OpenAI is reportedly considering granting CEO Sam Altman a 7% equity stake and restructuring into a for-profit business. This would be the first time Altman would hold ownership in the company. OpenAI is exploring becoming a public benefit corporation, balancing profit-making with societal benefits. Discussions are ongoing, with no set timeline for the transition, according to sources who requested anonymity due to the private nature of the information. A spokesperson for OpenAI emphasized that the nonprofit remains central to the company’s mission and will continue to operate.

Apple Walks Away from OpenAI Investment Deal, Microsoft and Nvidia Still Involved

Tech giant Apple has withdrawn from discussions to invest in OpenAI's latest funding round, which is valued at around $6.5 billion, according to a report from the Wall Street Journal on Friday.

The report, citing a source familiar with the situation, noted that Apple recently stepped back from the negotiations, which are expected to close next week. Other companies, including Microsoft and Nvidia, are reportedly still in talks to participate in the investment. Microsoft is expected to invest about $1 billion (approximately Rs. 8,379 crore) after having already invested $13 billion (around Rs. 1,08,928 crore) in OpenAI.

Neither OpenAI nor Apple have commented on the matter, and Apple did not immediately respond to a request for comment. Last month, the Wall Street Journal initially reported that Apple was in talks to join OpenAI’s fundraising effort, which could value the company behind ChatGPT at over $100 billion (roughly Rs. 8,37,996 crore).

This high valuation reflects the competitive landscape sparked by OpenAI’s launch of ChatGPT in late 2022. The AI race triggered by ChatGPT has led companies across various industries to invest billions in AI technology to stay competitive and secure their market share.

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