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Predictions for AI-powered digital healthcare in 2021
Oncology diagnosis inclusing digital pathology, telehealth and physician access are just some of the areas where AI can have outsize impact in 2021
By CHAIM LINHART
Post a comment / Dec 23, 2020 at 9:44 AM
Apart from efforts to develop vaccines for Covid-19, 2020 has been a year of substantial advancements in artificial intelligence (AI) and machine learning – technologies that are being applied in healthcare as never before, resulting in improvements and enhancements in diagnosis and treatment that were previously impossible.
These technologies are advancing at an increasing rate, and we can be sure that AI will be used even more to overcome healthcare challenges in 2021 and beyond. Here are some of the main areas AI will be used to enhance digital healthcare in the near future.
Oncology
Many healthcare practices today, including cancer diagnosis, still rely heavily on manual activities and processes – and even those that do utilize digital technologies use them on an independent basis, not under one integral environment. Pathology, the medical specialty of diagnosing disease in patients, most notably cancer, is a good example. Traditional pathology involves manual processes that have remained unchanged for years, where glass slides with tissue samples are analyzed by pathologists using microscopes.
But that is changing today with a growing trend of moving toward digitized infrastructures and workflows (for example digital pathology). This trend is expected to accelerate as a result of the Covid-19 pandemic, increasingly saving time and labor costs while providing better and more cost-effective care. Moreover, adding an AI layer to an already digitized workflow can help make processes even more efficient on several fronts, including:
Triage: AI tools can help determine which cases should be reviewed more urgently and which physicians or resources to assign to them. This will ensure that each case is diagnosed by the physician whose capabilities can best be applied to the situation at hand, assigning cases based on a physician’s sub-specialty or level of experience, which becomes especially important in complex cases.
Diagnosis: AI tools can also help with cancer diagnosis and assessment, pointing instantly to particular areas of interest, for instance those that include cancerous cells, and signaling the ‘needle in the haystack’ factor that can shed light on a patient’s situation – and what may be required to treat them. As a result, turnaround times for case reporting to the referring physician can be reduced significantly. In addition, smart AI tools will allow for the automation of some diagnostic tasks that are currently performed manually, such as counting cells, measuring features and automatically filling up parts of the report.
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Next-generation diagnostics: With advanced machine learning capabilities and as more digital datasets become available, AI tools will be able to analyze more data – and thus provide more insight – than is currently possible. These tools will go beyond mimicking a physician’s diagnosis; AI models will integrate extensive amounts of data from diverse sources (e.g., imaging, pathology and clinical data), acting as a sort of tumor board, where experts from multiple fields share their findings and knowledge to decide on the most accurate diagnosis and treatment. In the near future, we can expect to see AI serve as the “perfect companion” to the physician, with an unparalleled ability to combine huge amounts of fragmented information accurately and effectively.
Telehealth and Physician Access
The need for quality healthcare keeps growing with an aging population and an increase in disease incidence. The number of physicians, however, does not grow at the same rate, and this creates a supply gap particularly evident for certain medical sub-specialties, in far-flung rural communities and sometimes even in urban centers. AI-based telehealth – for instance, a new generation of affordable and accurate sensing devices that can be used to examine, monitor or diagnose patients remotely, as well as new communication technologies such as AI-based chatbots – will help transform a visit to the doctor’s office into a completely different experience, enabling a more effective, frequent and affordable physician access for patients. The pandemic, with its frequent quarantines and regional lockdowns, only demonstrated the importance of these technologies as well as the shift needed in the mindset of all stakeholders: patients, physicians, providers and payers.
Personalized Medicine
With all the advancements in medical science, prognostic assessments and treatment decisions are many times not much better than a shot in the dark. For example, up to 75% of oncology patients (in a number of cancer types) do not respond to at least one of the available treatment drugs. Developing new biomarkers and genomic tests to more accurately predict prognosis and enable physicians to choose the right treatment is a costly, unpredictable, and painfully slow process.
Enter AI. Biomarkers based on artificial intelligence and machine learning can be an important, cost-effective and efficient way to develop tools for precision medicine. AI algorithms could provide new prognostic insights and help oncologists stratify patients into smaller, more homogeneous groups and assist in selecting personalized treatment for each patient based on a multitude of data types. These algorithms will combine pathology, radiology, genomic, clinical and demographic data, and analyze huge databases of medical records, treatments and outcomes. AI-driven solutions could also provide a more accurate and objective analysis of medical data, leading to new computational tests that replace or augment today’s molecular tests.
Quality Control
Obviously an essential part of any quality-driven system, quality control is a real challenge in labor-intensive processes. For example, for pathologists diagnosing cancer, an effective “near bulletproof” quality control requires having a second pathologist review the same biopsy again — a time consuming and costly task. As a result, we find many clinical processes where quality control is done only on a small fraction of cases, if at all, often failing to detect errors.
AI and advanced algorithms can help create concurrent processes that rapidly review medical procedures and diagnoses, detecting mistakes early enough to avoid any damage. Fast and automated AI tools will enable pathology labs – for the first time – to apply rigid quality control standards to all their cases. Currently, the majority of cases are viewed by a single pathologist in almost all labs, which means that errors and misdiagnosed cancers may not be detected on time. AI can help transform this completely.
Algorithms can also alert on inconsistent findings, making sure the different medical disciplines provide a coherent picture of the patient’s condition and treatment plan, and that there are no “holes” in the process. An electronic medical record (EMR) that contains all the medical information of a patient could be constantly monitored by AI – whenever something suspicious is detected, such as a lab test result that is inconsistent with a previous diagnosis, or a drug prescription that does not match the patient’s condition, the AI would highlight the inconsistency, prompting a thorough review by the relevant physician.
Screening Protocols
Decisions on issues like which patient should be sent for a specific test – CT, MRI, colonoscopy and others – are currently conducted based on demographics or other general variables that often have nothing to do with a patient’s medical record. Going forward, we will see more medical screening that’s based on specific characteristics identified by AI algorithms trained on large providers’ datasets. Such predictive algorithms will identify specific patterns, risk factors or correlations between multiple variables using advanced machine learning techniques.
Science-based medicine is one of the miracles of the modern age; lives are being saved today that just a few years ago would have been unsalvageable, and new, advanced treatments are providing a much better quality of life for many more than would have been possible in the past. Still, there are many domains in medicine and healthcare that could benefit from adopting the latest developments in artificial intelligence and machine learning and we see their role in improving healthcare becoming ever more important in the future.
Chaim Linhart
Chaim Linhart
Chaim Linhart is the CTO and Co-Founder of Ibex Medical Analytics. He has more than 25 years of experience in algorithm development, AI and machine learning from academia as well as serving in an elite unit in the Israeli military and at several tech companies. Chaim has a PhD in Computer Science from Tel Aviv University and has won multiple Kaggle machine learning competitions.
This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.
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Predictions for AI-powered digital healthcare in 2021
Oncology diagnosis inclusing digital pathology, telehealth and physician access are just some of the areas where AI can have outsize impact in 2021
By CHAIM LINHART
Post a comment / Dec 23, 2020 at 9:44 AM
Apart from efforts to develop vaccines for Covid-19, 2020 has been a year of substantial advancements in artificial intelligence (AI) and machine learning – technologies that are being applied in healthcare as never before, resulting in improvements and enhancements in diagnosis and treatment that were previously impossible.
These technologies are advancing at an increasing rate, and we can be sure that AI will be used even more to overcome healthcare challenges in 2021 and beyond. Here are some of the main areas AI will be used to enhance digital healthcare in the near future.
Oncology
Many healthcare practices today, including cancer diagnosis, still rely heavily on manual activities and processes – and even those that do utilize digital technologies use them on an independent basis, not under one integral environment. Pathology, the medical specialty of diagnosing disease in patients, most notably cancer, is a good example. Traditional pathology involves manual processes that have remained unchanged for years, where glass slides with tissue samples are analyzed by pathologists using microscopes.
But that is changing today with a growing trend of moving toward digitized infrastructures and workflows (for example digital pathology). This trend is expected to accelerate as a result of the Covid-19 pandemic, increasingly saving time and labor costs while providing better and more cost-effective care. Moreover, adding an AI layer to an already digitized workflow can help make processes even more efficient on several fronts, including:
Triage: AI tools can help determine which cases should be reviewed more urgently and which physicians or resources to assign to them. This will ensure that each case is diagnosed by the physician whose capabilities can best be applied to the situation at hand, assigning cases based on a physician’s sub-specialty or level of experience, which becomes especially important in complex cases.
Diagnosis: AI tools can also help with cancer diagnosis and assessment, pointing instantly to particular areas of interest, for instance those that include cancerous cells, and signaling the ‘needle in the haystack’ factor that can shed light on a patient’s situation – and what may be required to treat them. As a result, turnaround times for case reporting to the referring physician can be reduced significantly. In addition, smart AI tools will allow for the automation of some diagnostic tasks that are currently performed manually, such as counting cells, measuring features and automatically filling up parts of the report.
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Next-generation diagnostics: With advanced machine learning capabilities and as more digital datasets become available, AI tools will be able to analyze more data – and thus provide more insight – than is currently possible. These tools will go beyond mimicking a physician’s diagnosis; AI models will integrate extensive amounts of data from diverse sources (e.g., imaging, pathology and clinical data), acting as a sort of tumor board, where experts from multiple fields share their findings and knowledge to decide on the most accurate diagnosis and treatment. In the near future, we can expect to see AI serve as the “perfect companion” to the physician, with an unparalleled ability to combine huge amounts of fragmented information accurately and effectively.
Telehealth and Physician Access
The need for quality healthcare keeps growing with an aging population and an increase in disease incidence. The number of physicians, however, does not grow at the same rate, and this creates a supply gap particularly evident for certain medical sub-specialties, in far-flung rural communities and sometimes even in urban centers. AI-based telehealth – for instance, a new generation of affordable and accurate sensing devices that can be used to examine, monitor or diagnose patients remotely, as well as new communication technologies such as AI-based chatbots – will help transform a visit to the doctor’s office into a completely different experience, enabling a more effective, frequent and affordable physician access for patients. The pandemic, with its frequent quarantines and regional lockdowns, only demonstrated the importance of these technologies as well as the shift needed in the mindset of all stakeholders: patients, physicians, providers and payers.
Personalized Medicine
With all the advancements in medical science, prognostic assessments and treatment decisions are many times not much better than a shot in the dark. For example, up to 75% of oncology patients (in a number of cancer types) do not respond to at least one of the available treatment drugs. Developing new biomarkers and genomic tests to more accurately predict prognosis and enable physicians to choose the right treatment is a costly, unpredictable, and painfully slow process.
Enter AI. Biomarkers based on artificial intelligence and machine learning can be an important, cost-effective and efficient way to develop tools for precision medicine. AI algorithms could provide new prognostic insights and help oncologists stratify patients into smaller, more homogeneous groups and assist in selecting personalized treatment for each patient based on a multitude of data types. These algorithms will combine pathology, radiology, genomic, clinical and demographic data, and analyze huge databases of medical records, treatments and outcomes. AI-driven solutions could also provide a more accurate and objective analysis of medical data, leading to new computational tests that replace or augment today’s molecular tests.
Quality Control
Obviously an essential part of any quality-driven system, quality control is a real challenge in labor-intensive processes. For example, for pathologists diagnosing cancer, an effective “near bulletproof” quality control requires having a second pathologist review the same biopsy again — a time consuming and costly task. As a result, we find many clinical processes where quality control is done only on a small fraction of cases, if at all, often failing to detect errors.
AI and advanced algorithms can help create concurrent processes that rapidly review medical procedures and diagnoses, detecting mistakes early enough to avoid any damage. Fast and automated AI tools will enable pathology labs – for the first time – to apply rigid quality control standards to all their cases. Currently, the majority of cases are viewed by a single pathologist in almost all labs, which means that errors and misdiagnosed cancers may not be detected on time. AI can help transform this completely.
Algorithms can also alert on inconsistent findings, making sure the different medical disciplines provide a coherent picture of the patient’s condition and treatment plan, and that there are no “holes” in the process. An electronic medical record (EMR) that contains all the medical information of a patient could be constantly monitored by AI – whenever something suspicious is detected, such as a lab test result that is inconsistent with a previous diagnosis, or a drug prescription that does not match the patient’s condition, the AI would highlight the inconsistency, prompting a thorough review by the relevant physician.
Screening Protocols
Decisions on issues like which patient should be sent for a specific test – CT, MRI, colonoscopy and others – are currently conducted based on demographics or other general variables that often have nothing to do with a patient’s medical record. Going forward, we will see more medical screening that’s based on specific characteristics identified by AI algorithms trained on large providers’ datasets. Such predictive algorithms will identify specific patterns, risk factors or correlations between multiple variables using advanced machine learning techniques.
Science-based medicine is one of the miracles of the modern age; lives are being saved today that just a few years ago would have been unsalvageable, and new, advanced treatments are providing a much better quality of life for many more than would have been possible in the past. Still, there are many domains in medicine and healthcare that could benefit from adopting the latest developments in artificial intelligence and machine learning and we see their role in improving healthcare becoming ever more important in the future.
Chaim Linhart
Chaim Linhart
Chaim Linhart is the CTO and Co-Founder of Ibex Medical Analytics. He has more than 25 years of experience in algorithm development, AI and machine learning from academia as well as serving in an elite unit in the Israeli military and at several tech companies. Chaim has a PhD in Computer Science from Tel Aviv University and has won multiple Kaggle machine learning competitions.
This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.
TOPICS
artificial intelligence in healthcare, biopharma nl
Health on the Web
Ads By logo
img-responsive
Healthcare Marketers Are Now Using Native Ads Contextually!
Tap Native Health Marketing
img-responsive
Healthcare Pros Showcase Credentials to Discover New Careers
TruVerify
Hear the latest industry news first. Sign up for our daily newsletter.
Your email address*
We will never sell or share your information without your consent. See our privacy policy.
WE RECOMMEND
Japanese firm’s AI algorithm for gastric cancer gets FDA’s Breakthrough Device designation
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DoD taps Google Cloud for digital pathology pilot
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The global pandemic is pushing technology forward in unexpected ways
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You searched for "digital pathology" - MedCity News
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Boston-based AI pathology startup rakes in $60M Series B round
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Cardiac amyloid imaging with 18F-florbetaben positron emission tomography: a pilot study
W. Phillip Law et al., J Nucl Med, 2016
Cerebellar amyloid-beta plaques: How frequent are they, and do they influence 18F-Florbetaben SUVR?
Ana M Catafau et al., J Nucl Med, 2016
The Heart of the Matter: An In-Depth Look at the Latest Evidence With CLL Therapies and How to Optimize Treatment Safety
An independent learning activity funded by Janssen Inc. 15minutes
MONARCH 2: Abemaciclib in Combination With Fulvestrant in Women With HR+/HER2− Advanced Breast Cancer Who Had Progressed While Receiving Endocrine Therapy
Sledge et al., J Clin Oncol, 2017
CytoDyn Seeking to Expand Enrollment in Study Based on Promising Leronlimab Data
Precision Oncology News, 2020
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