(PDF) A Review of Applications of Artificial Intelligence in Heavy Duty Trucks
Why Woebot, A Pioneering Therapy Chatbot, Shut Down
Woebot Health this week shut down its core product, a pioneering therapy chatbot. Its demise was hastened by the new wave of conversational artificial intelligence that Woebot foreshadowed.
The smartphone app starred a cartoony bot that guided people through conversations meant to address anxiety or help them cope with everyday problems, using techniques rooted in cognitive behavioral therapy. Roughly 1.5 million people used Woebot over the years, and although it had the interactive feel of ChatGPT and similar generative AI products, the bot's responses and behavior were pre-scripted. It was an impressive chatbot before more advanced technology was available.
Woebot founder and CEO Alison Darcy told STAT over a series of interviews that shutting down the app is largely attributable to the cost and challenge of fulfilling the Food and Drug Administration's requirements for marketing authorization. But the company's need to move on was made more pressing by the advent of large language models that the company wanted to use, but that the FDA hasn't yet figured out how to regulate.
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SubscribeGino Tesei: Harnessing AI To Transform Healthcare From The Inside Out
As artificial intelligence (AI) reshapes industries across the globe, few sectors show as much potential for impact as healthcare. Data scientists at the intersection of medicine and machine learning (ML) now help determine how care is delivered, personalized, and improved. Gino Tesei, principal data scientist at Elevance Health, is a recognized leader in AI-driven healthcare solutions, combining technical brilliance and real-world application to drive innovation.
"I want to make a difference in healthcare by using artificial intelligence," Tesei says. "The next frontier is generating synthetic data that faithfully represents the full diversity of our communities, and that's exactly where I'm focused."
Designing the Future of AI in HealthcareSince 2020, Tesei has led generative AI efforts at Elevance Health, developing systems that improve efficiency and build AI capabilities for both employees and members. "I work on organizational improvements, publish peer-reviewed research in precision medicine, and develop AI tools, such as conversational chatbots, that transform those insights into real-world applications," he explains. "I also coach support teams and mentor data scientists, guiding them to build new skills and advance their careers."
Before joining Elevance, Tesei was a manager and lead data scientist at Deloitte. There, he specialized in automation and AI for large pharmaceutical companies. "I served on the AI Center of Excellence for a leading pharmaceutical client," he recalls. "I led key research initiatives and consistently earned the highest level of recognition in annual reviews."
Earlier in his career, Tesei made headlines in Italy. While working as a technical leader at Telecom Italia, he designed the country's number-one news search engine. "It handled 60 million searches per day and outperformed Google Italia," he says. "That's still one of my proudest achievements."
A Lifelong Learner With Elite CredentialsTesei's academic path helped set the foundation for his work. He holds a master's degree in engineering of computing systems from Politecnico di Milano, graduating cum laude. He later earned an Executive Master of Business Administration from SDA Bocconi School of Management. "Education gave me the technical and strategic tools I needed," he says. "But mentors also played a huge role, especially my elementary school teacher and my first CEO in finance."
Tesei has received numerous certifications, including graduate certificates in AI and ML from Stanford University. "I embrace continuous learning to keep my skills sharp and maintain a competitive edge," Tesei says.
When he's not designing AI systems, Tesei stays active: "I enjoy reading, running, exercising and traveling. And, of course, I love good fine food and wine." He also gives back through civic efforts. "I volunteer with the San Francisco Italian Athletic Club helping lead initiatives such as scholarship programs for disadvantaged students."
Looking Forward: Empowering Patients With AITesei's next goal is to put the power of AI directly in patients' hands. "My vision is to build an intelligent platform that analyses each person's medical history and real-time health data, then matches them with the specialists best equipped to treat their exact condition," he explains. "It's about empowering them and improving their overall journey through data."
With a blend of vision and precision, Tesei continues to shape the future of healthcare, one algorithm at a time.
Jordan French is the Founder and Executive Editor of Grit Daily Group , encompassing Financial Tech Times, Smartech Daily, Transit Tomorrow, BlockTelegraph, Meditech Today, High Net Worth magazine, Luxury Miami magazine, CEO Official magazine, Luxury LA magazine, and flagship outlet, Grit Daily. The champion of live journalism, Grit Daily's team hails from ABC, CBS, CNN, Entrepreneur, Fast Company, Forbes, Fox, PopSugar, SF Chronicle, VentureBeat, Verge, Vice, and Vox. An award-winning journalist, he was on the editorial staff at TheStreet.Com and a Fast 50 and Inc. 500-ranked entrepreneur with one sale. Formerly an engineer and intellectual-property attorney, his third company, BeeHex, rose to fame for its "3D printed pizza for astronauts" and is now a military contractor. A prolific investor, he's invested in 50+ early stage startups with 10+ exits through 2023.
Automation In Healthcare & Life Sciences: How It Helps And What's Next
Accelerating Clinical Trials Through Automation and AIThe recruitment phase for clinical trials takes an average of 18 months, and nearly 20% of cancer trials fail because of low accrual rates. Automation and AI can improve this process — and help bring lifesaving treatments to patients faster — by identifying and recruiting eligible participants.
Robotic process automation tools can be especially beneficial in this area by assessing patient records and matching them to appropriate trials.
"Medical abstraction can be tedious and expensive. In clinical trial matching, structuring trial eligibility is easy, whereas structuring patient records is the real bottleneck," says Poon. He cites Microsoft's Healthcare Agent Orchestrator as an example of "how RPA can potentially unlock massive productivity gains by introducing agents to automate information gathering, normalization, integration and clinical trial matching scenarios."
Intelligent document processing tools are also proving beneficial. IDP can help research teams avoid manual errors, improve patient data accuracy, and more efficiently analyze massive volume sets. Amazon Web Services points out that when
The TrialGPT algorithm, developed at the National Institutes of Health, is an example of this type of technology. In a pilot study, researchers found that when assessing patients for trial eligibility, TrialGPT spent 40% less time on screening but achieved the same level of accuracy as human clinicians. TrialGPT also created summaries explaining why a patient was a good fit for a trial.
EXPLORE: Here are 13 ways AI enhances healthcare operations, patient care and treatments.
The Role of Cloud and Advanced Analytics in Drug Discovery"We and others have already used AI systems to generate promising drug candidates, and I expect such successes to rapidly accumulate in the next few years," Poon says. "We can shrink the time for target identification, lead compound identification and optimization."
Researchers say the drug discovery phase, which typically takes three to six years and accounts for about 35% of the total cost of developing a new treatment, can be shortened by one or two years with AI. That's because AI can identify and test the effects of different compounds faster than a human can.
Advanced data analytics are critical for this type of analysis. AI-powered algorithms can analyze and compare massive amounts of information across multiple databases to identify which combinations will be most effective in creating a new drug.
This type of work wouldn't be possible without cloud computing and storage. While on-premises data centers have a defined amount of space, the cloud gives life sciences organizations the unlimited scale they may need to manage and analyze these large data sets.
The cloud also allows organizations to adjust their storage capabilities — and therefore, better control costs — by partnering with vendors for access to powerful graphics processing units and CPUs.
"Let's say you're working in a Google Cloud environment and using their high-performance computing to run protein folding scenarios," says Joe Miles, industry director of life sciences at UiPath. "You can then take that information and route it to appropriate repositories as it pertains to an individual trial."
Streamlining Documentation and Revenue Cycle Management With Agentic AILife sciences is a highly regulated industry involving substantial paperwork. Drafting a regulatory submission to the U.S. Food and Drug Administration for a new drug or device may take several months, and the agency has specific format and content requirements. Agentic AI can help organizations more efficiently handle the red tape, which frees researchers to focus on the actual science.
"We see automation being used for regulatory submissions quite a bit, especially for clinical trials," says Miles. "AI agents can review the documents specific to all of the defined protocols and help with the formatting and syntax. Making sure all of the support documentation in place goes directly to reducing time to market."
Miles adds that agentic AI can help with revenue cycle management; for example, by automatically processing routine invoices and sales orders. Agents can also help monitor email inboxes and flag potential problems that require human review.
"An example is if an adverse event form came in that needs to be routed immediately to the appropriate individual," Miles explains. "Intelligent document processing is intertwined in that process, in the ability to read an email and understand the sentiment."
DISCOVER: Cloud-based HPC is helping researchers move healthcare forward.
What's Next for Digital Transformation in Medical ResearchThe role of automation and AI is expected to continue growing at a significant rate. In NVIDIA's survey of life sciences and healthcare companies, 78% of respondents said their organizations planned to increase their budgets for AI infrastructure.
Miles anticipates an increasing focus on agentic AI. "Because of their ability to make solid decisions based on contextual information, I think we'll see more agent releases," Miles says. "I think we'll see networks of agents that manage processes at an organic level and allow people to really focus on the research and the more challenging subjects."
Poon adds that while automation and AI are improving operational efficiencies, he projects that these advanced technologies will help researchers generate valuable solutions that transform healthcare.
"Transformation will start from productivity gains, which are already happening in the text modality with frontier AI," says Poon. "We still need major research breakthroughs to bridge competency gaps in multimodal and longitudinal patient modeling, but there is rapid progress."
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