Google has unveiled MedLM, a collection of new AI models tailored to the healthcare industry that will assist academics and physicians in conducting intricate investigations, summarising patient-provider interactions, and other tasks.
Google's latest attempt to monetize AI technologies for the healthcare industry comes at a time when rivals like Amazon and Microsoft are still vying for market dominance. Although they are taking precautions to adopt it cautiously, experts say there is substantial potential for effect, as demonstrated by the companies who have been testing Google's technology, such as HCA Healthcare.
A big and a medium-sized AI model are part of the MedLM suite. They are both based on Med-PaLM 2, a large language model that Google initially unveiled in March and was trained on medical data.
The medium-sized model is less expensive to run, according to Google, although the cost of the AI suite varies based on how businesses use the various models. It is generally available to qualified Google Cloud customers in the U.S. starting on Wednesday.
In the future, MedLM will be able to use Gemini, the company's newest and "most capable" AI model, in versions tailored to the healthcare industry, according to Google.
Google Cloud's worldwide head of health-care strategy and solutions, Aashima Gupta, reported that the business discovered that several medically customised AI models are more effective at doing specific jobs than others. Google made the decision to launch a range of models rather than attempting to create a "one-size-fits-all" remedy because of this.
For example, Google stated that its more expansive MedLM model performs better when handling complex activities requiring extensive computational capacity and in-depth expertise, such completing a research with patient data from a health-care organisation as a whole. However, Gupta says the medium-sized model should perform better if businesses require a more flexible model that can be tailored for particular or real-time tasks, such summarising a patient-doctor conversation.
In its first announcement, Google stated that Med-PaLM 2 might be used to respond to queries such as "What are the early warning signs of pneumonia?" and "Is there a cure for incontinence?However, according to Greg Corrado, head of Google's health AI, "the use cases have changed as the company has tested the technology with customers."
According to Corrado, there hasn't been much customer demand for Google to provide "accessible" answers to questions regarding the nature of diseases because clinicians don't typically need assistance with them. Rather, AI is frequently requested by health organisations to assist in resolving more administrative or back-office issues, such as document management.
“They want something that’s helping them with the real pain points and slowdowns that are in their workflow, that only they know,” Corrado said.
For example, since the spring, one of the biggest health systems in the United States, HCA Healthcare, has been testing Google's AI technology. In August, the business formally announced a partnership with Google Cloud, with the goal of leveraging the latter's generative AI to "improve workflows on time-consuming tasks."
HCA's senior vice president of care transformation and innovation, Dr. Michael Schlosser, stated that MedLM has been used by the organisation to assist emergency medicine doctors in automatically recording their patient encounters. For example, HCA transcribes doctor-patient meetings using an ambient voice documentation technology from Augmedix. These transcripts can then be divided into the sections of an ER physician note using Google's MedLM suite.
Schlosser stated that HCA has been implementing MedLM in four hospitals' emergency rooms, and the business plans to increase its use in the coming year. Schlosser continued, "I anticipate that by January, Google's technology will be able to generate more than half of a note successfully without assistance from providers." Schlosser stated that doctors can devote as much as four hours a day on administrative paperwork; therefore, saving them time and effort is significant.
“That’s been a huge leap forward for us,” Schlosser said. “We now think we’re going to be at a point where the AI, by itself, can create 60-plus percent of the note correctly on its own before we have the human doing the review and the editing.”
Schlosser stated that HCA is also developing a nurse handoff tool using MedLM. The device has the ability to scan an electronic health record and find pertinent data that nurses can forward to the following shift.
Automating the handoff process would be "powerful" because it is "laborious" and a major source of discomfort for nurses, according to Schlosser. Approximately 400,000 handoffs are performed by nurses each week amongst the hospitals owned by HCA, and two of those hospitals have been testing a nurse handoff tool. According to Schlosser, nurses compare traditional and AI-generated handoffs side by side and offer comments.
However, HCA has discovered that MedLM is not infallible in both use circumstances.
The fact that AI models can provide false information, according to Schlosser, is a significant concern. HCA and Google have been collaborating to develop best practices to reduce these fabrications. He continued by saying that controlling the AI over time and token constraints, which limit the amount of data that can be fed into the model, have been additional hurdles for HCA.
“What I would say right now, is that the hype around the current use of these AI models in health care is outstripping the reality,” Schlosser said. “Everyone’s contending with this problem, and no one has really let these models loose in a scaled way in the health-care systems because of that.”
Nevertheless, according to Schlosser, providers have responded well to MedLM thus far and are aware that they are not yet using the final product. He claimed that in order to prevent endangering patients, HCA is making a lot of effort to deploy the technology responsibly.
“We’re being very cautious with how we approach these AI models,” he said. “We’re not using those use cases where the model outputs can somehow affect someone’s diagnosis and treatment.”
In the future, Google intends to roll out MedLM-specific versions of Gemini. Following the launch of Gemini earlier this month, the company's shares surged by 5%; however, Google came under fire for its demonstration video, which was not recorded in real time, as the company revealed to Bloomberg.
In a statement, Google said: “The video is an illustrative depiction of the possibilities of interacting with Gemini, based on real multimodal prompts and outputs from testing. We look forward to seeing what people create when access to Gemini Pro opens on December 13.”
Google executives Corrado and Gupta stated that Gemini is still in its infancy and that before the concept is implemented more widely through MedLM, it must be tried and tested with patients in regulated medical environments.
“We’ve been testing Med-PaLM 2 with our customers for months, and now we’re comfortable taking that as part of MedLM,” Gupta said. “Gemini will follow the same thing.”
Schlosser stated that HCA is "very excited" about Gemini and that preparations to test the technology are already being developed by the corporation. When we receive that, "we think that might give us an extra level of performance," he remarked.
BenchSci is another firm that has been employing MedLM. Its goal is to apply AI to solve drug discovery challenges. BenchSci is backed by Google, and for the past three months, the business has been testing its MedLM technology.
The CEO and co-founder of BenchSci, Liran Belenzon, stated that the business has integrated MedLM's AI with BenchSci's own technology to assist scientists in identifying biomarkers, which are essential for comprehending the course of a disease and potential treatments.
According to Belenzon, the business invested a significant amount of effort in validating and testing the model, as well as informing Google of any enhancements that were required. Belenzon stated that BenchSci is currently working on expanding the technology's commercial reach.
″[MedLM] doesn’t work out of the box, but it helps accelerate your specific efforts,” he said in an interview.
MedLM research is still under progress, according to Corrado, who also believes that Google Cloud's health-care clients will be able to customise models for a variety of internal use cases. Google will keep creating domain-specific models that are "smaller, cheaper, faster, and better," he continued.
Similar to BenchSci, MedLM was "over and over" tested by Deloitte prior to being made available to healthcare clients, according to Dr. Kulleni Gebreyes, head of the company's life sciences and health care consulting practice in the United States.
Deloitte is assisting health systems and health plans in responding to member inquiries regarding care access by leveraging Google's technologies. For example, if a patient needs a colonoscopy, they can utilise MedLM to find providers based on eligibility requirements such as location, gender, or benefit coverage.
According to Gebreyes, customers have found MedLM to be accurate and effective; nevertheless, similar to other models, the AI occasionally struggles to interpret the user's purpose. According to her, patients who use other slang words or don't know the correct spelling for colonoscopy can provide difficulties.
“Ultimately, this does not substitute a diagnosis from a trained professional,” Gebreyes said. “It brings expertise closer and makes it more accessible.”
(Source:www.medium.com)
Google's latest attempt to monetize AI technologies for the healthcare industry comes at a time when rivals like Amazon and Microsoft are still vying for market dominance. Although they are taking precautions to adopt it cautiously, experts say there is substantial potential for effect, as demonstrated by the companies who have been testing Google's technology, such as HCA Healthcare.
A big and a medium-sized AI model are part of the MedLM suite. They are both based on Med-PaLM 2, a large language model that Google initially unveiled in March and was trained on medical data.
The medium-sized model is less expensive to run, according to Google, although the cost of the AI suite varies based on how businesses use the various models. It is generally available to qualified Google Cloud customers in the U.S. starting on Wednesday.
In the future, MedLM will be able to use Gemini, the company's newest and "most capable" AI model, in versions tailored to the healthcare industry, according to Google.
Google Cloud's worldwide head of health-care strategy and solutions, Aashima Gupta, reported that the business discovered that several medically customised AI models are more effective at doing specific jobs than others. Google made the decision to launch a range of models rather than attempting to create a "one-size-fits-all" remedy because of this.
For example, Google stated that its more expansive MedLM model performs better when handling complex activities requiring extensive computational capacity and in-depth expertise, such completing a research with patient data from a health-care organisation as a whole. However, Gupta says the medium-sized model should perform better if businesses require a more flexible model that can be tailored for particular or real-time tasks, such summarising a patient-doctor conversation.
In its first announcement, Google stated that Med-PaLM 2 might be used to respond to queries such as "What are the early warning signs of pneumonia?" and "Is there a cure for incontinence?However, according to Greg Corrado, head of Google's health AI, "the use cases have changed as the company has tested the technology with customers."
According to Corrado, there hasn't been much customer demand for Google to provide "accessible" answers to questions regarding the nature of diseases because clinicians don't typically need assistance with them. Rather, AI is frequently requested by health organisations to assist in resolving more administrative or back-office issues, such as document management.
“They want something that’s helping them with the real pain points and slowdowns that are in their workflow, that only they know,” Corrado said.
For example, since the spring, one of the biggest health systems in the United States, HCA Healthcare, has been testing Google's AI technology. In August, the business formally announced a partnership with Google Cloud, with the goal of leveraging the latter's generative AI to "improve workflows on time-consuming tasks."
HCA's senior vice president of care transformation and innovation, Dr. Michael Schlosser, stated that MedLM has been used by the organisation to assist emergency medicine doctors in automatically recording their patient encounters. For example, HCA transcribes doctor-patient meetings using an ambient voice documentation technology from Augmedix. These transcripts can then be divided into the sections of an ER physician note using Google's MedLM suite.
Schlosser stated that HCA has been implementing MedLM in four hospitals' emergency rooms, and the business plans to increase its use in the coming year. Schlosser continued, "I anticipate that by January, Google's technology will be able to generate more than half of a note successfully without assistance from providers." Schlosser stated that doctors can devote as much as four hours a day on administrative paperwork; therefore, saving them time and effort is significant.
“That’s been a huge leap forward for us,” Schlosser said. “We now think we’re going to be at a point where the AI, by itself, can create 60-plus percent of the note correctly on its own before we have the human doing the review and the editing.”
Schlosser stated that HCA is also developing a nurse handoff tool using MedLM. The device has the ability to scan an electronic health record and find pertinent data that nurses can forward to the following shift.
Automating the handoff process would be "powerful" because it is "laborious" and a major source of discomfort for nurses, according to Schlosser. Approximately 400,000 handoffs are performed by nurses each week amongst the hospitals owned by HCA, and two of those hospitals have been testing a nurse handoff tool. According to Schlosser, nurses compare traditional and AI-generated handoffs side by side and offer comments.
However, HCA has discovered that MedLM is not infallible in both use circumstances.
The fact that AI models can provide false information, according to Schlosser, is a significant concern. HCA and Google have been collaborating to develop best practices to reduce these fabrications. He continued by saying that controlling the AI over time and token constraints, which limit the amount of data that can be fed into the model, have been additional hurdles for HCA.
“What I would say right now, is that the hype around the current use of these AI models in health care is outstripping the reality,” Schlosser said. “Everyone’s contending with this problem, and no one has really let these models loose in a scaled way in the health-care systems because of that.”
Nevertheless, according to Schlosser, providers have responded well to MedLM thus far and are aware that they are not yet using the final product. He claimed that in order to prevent endangering patients, HCA is making a lot of effort to deploy the technology responsibly.
“We’re being very cautious with how we approach these AI models,” he said. “We’re not using those use cases where the model outputs can somehow affect someone’s diagnosis and treatment.”
In the future, Google intends to roll out MedLM-specific versions of Gemini. Following the launch of Gemini earlier this month, the company's shares surged by 5%; however, Google came under fire for its demonstration video, which was not recorded in real time, as the company revealed to Bloomberg.
In a statement, Google said: “The video is an illustrative depiction of the possibilities of interacting with Gemini, based on real multimodal prompts and outputs from testing. We look forward to seeing what people create when access to Gemini Pro opens on December 13.”
Google executives Corrado and Gupta stated that Gemini is still in its infancy and that before the concept is implemented more widely through MedLM, it must be tried and tested with patients in regulated medical environments.
“We’ve been testing Med-PaLM 2 with our customers for months, and now we’re comfortable taking that as part of MedLM,” Gupta said. “Gemini will follow the same thing.”
Schlosser stated that HCA is "very excited" about Gemini and that preparations to test the technology are already being developed by the corporation. When we receive that, "we think that might give us an extra level of performance," he remarked.
BenchSci is another firm that has been employing MedLM. Its goal is to apply AI to solve drug discovery challenges. BenchSci is backed by Google, and for the past three months, the business has been testing its MedLM technology.
The CEO and co-founder of BenchSci, Liran Belenzon, stated that the business has integrated MedLM's AI with BenchSci's own technology to assist scientists in identifying biomarkers, which are essential for comprehending the course of a disease and potential treatments.
According to Belenzon, the business invested a significant amount of effort in validating and testing the model, as well as informing Google of any enhancements that were required. Belenzon stated that BenchSci is currently working on expanding the technology's commercial reach.
″[MedLM] doesn’t work out of the box, but it helps accelerate your specific efforts,” he said in an interview.
MedLM research is still under progress, according to Corrado, who also believes that Google Cloud's health-care clients will be able to customise models for a variety of internal use cases. Google will keep creating domain-specific models that are "smaller, cheaper, faster, and better," he continued.
Similar to BenchSci, MedLM was "over and over" tested by Deloitte prior to being made available to healthcare clients, according to Dr. Kulleni Gebreyes, head of the company's life sciences and health care consulting practice in the United States.
Deloitte is assisting health systems and health plans in responding to member inquiries regarding care access by leveraging Google's technologies. For example, if a patient needs a colonoscopy, they can utilise MedLM to find providers based on eligibility requirements such as location, gender, or benefit coverage.
According to Gebreyes, customers have found MedLM to be accurate and effective; nevertheless, similar to other models, the AI occasionally struggles to interpret the user's purpose. According to her, patients who use other slang words or don't know the correct spelling for colonoscopy can provide difficulties.
“Ultimately, this does not substitute a diagnosis from a trained professional,” Gebreyes said. “It brings expertise closer and makes it more accessible.”
(Source:www.medium.com)
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