Contributed: Nine revolutionary ways AI is advancing healthcare

The emergence of artificial intelligence (AI) in healthcare is the result of increased demand for tailored healthcare solutions, lower costs, higher accuracy pertaining to medical decisions and diagnosis to improve patient health outcomes. 

With an estimated valuation of $15.1 billion for the global market in 2022, AI in healthcare is an irreversible process that is already reshaping medicine and its projected value will continue to rise in the following years. 

Here are the top nine revolutionary ways AI is advancing healthcare:

1. Medical imaging 

AI algorithms can analyze complex medical images from computed tomography (CT) scans to X-rays and magnetic resonance imaging (MRI) which helps: 

  • identify brain tumors by checking MRI scans.

  • diagnosis cerebrovascular disease by analyzing CT images to empower timely triage and treatment.

  • recognize early-stage Alzheimer’s disease and dementia by analyzing brain scans and spotting changes in the brain’s structure and volume.

  • detect early-stage diabetic retinopathy by scanning retinal images

  • identify diseases such as pneumonia and tuberculosis

  • spot lung nodules in CT scans

  • detect osteoporosis by analyzing X-rays

AI algorithms provide clinicians with vital insights regarding the condition of the patients, increasing the speed of diagnosis, accuracy of medical results and improving patients’ health outcomes.  

Companies like Ezra use full-body MRI scans to assist medical professionals in early detection of cancer, while Zebra Medical Vision uses AI-driven tools to detect potential osteoporosis in X-rays and potential breast cancer in mammograms. 

 2. Surgery 

In recent years, AI-powered robots have become a common presence in operating rooms. These robots can conduct tasks that require precision and control, hence, supporting surgeons during complex operations, including open-heart surgery.  

Robots are equipped with mechanical arms, surgical instruments and cameras, and controlled by surgeons from a computer console. This provides surgeons with a three-dimensional, magnified view of difficult or impossible-to-see surgical sites and enables doctors to improve their skills, knowledge and experience.  

The use of AI-driven robots during surgery improves the chances for successful procedures, resulting in lesser complications for patients, shorter recovery periods and less pain after surgery.  

AI also comes to the aid of surgeons in other ways. For example, Theator’s Surgical Intelligence Platform analyzes thousands of hours of surgical videos, structures data from hundreds of procedures and helps surgeons understand what went right and what did not during operations. This work is used by surgeons to improve their skills and techniques, help save lives and achieve better health outcomes for the patients

3. Medical research and data analysis

In medical research, scientists collect and analyze huge amounts of data using statistical methods. Given that this process is often time-consuming and costly, adopting AI algorithms can accelerate the research by optimizing study design, patient recruitment and revealing deeper insights about diseases and treatments.  

Additionally, AI’s role extends to the analysis of patient records and clinical trial results to establish the effectiveness of new cancer treatments. By employing AI algorithms, researchers can pinpoint specific genetic markers that indicate which patients are most likely to have a positive response to treatment. This stratification could minimize the number of patients who would not benefit from certain treatments, leading to personalized therapies and improving the healthcare outcomes of those needing treatment. 

Building on these advancements, in 2022, Bayer investigated how AI algorithms can revolutionize clinical trials by creating virtual control groups to decrease or remove the need for “real” control groups in certain clinical trials. This way, the control groups in clinical trials would select fewer patients for placebo or standard treatment, thereby increasing the cost-efficiency of drug development, paving the way for smarter, faster and more patient-centric medical research.

4. Drug development 

Drug development is an expensive and long process, but AI can fix these issues: Machine Learning algorithms analyze vast datasets like genomic data connected to a disease, detect potential drug targets and predict drug’s efficacy and its potential side effects.

The same algorithms can examine in detail the available scientific literature and support the identification of genetic biomarkers that assess disease, enabling more effective clinical trials and shorter periods to put treatments on the market. 

Moreover, artificial intelligence enables researchers to analyze and repurpose existing medicines to combat specific diseases, making the development of new drugs more cost-efficient and effective. The emerging generative AI is accelerating drug discovery through designing molecular structures.

Biopharma company NuMedii has developed Artificial Intelligence for Drug Discovery (AIDD) technology that “employs deep learnings of human biology consisting of hundreds of millions of structured molecular, pharmacological and clinical data points that the company has curated and harmonized. The company couples these data with proprietary machine learning and network-based algorithms to discover and advance precise, effective new drug candidates, as well as biomarkers predictive of efficacy for subsets of patients, in a broad spectrum of therapeutic areas including orphan diseases like” idiopathic pulmonary fibrosis. 

5. Early detection of fatal blood diseases 

AI technology identifies changes in blood cells, a potential indicator of a blood disease. In leukemia, for example, algorithms can analyze patients’ medical history, blood cell morphology and genetic data then highlight patterns so subtle that they can be overlooked by human processing. This prompts AI-driven tools to assist medical professionals by “flagging” the presence of potential signs of leukemia, in the early stage. 

Moreover, AI can monitor changes that occur in blood cell counts in time and improve accuracy in detecting disease markers.  

Scopio Labs for example is the developer of full-field cell morphology – an AI-driven imaging platform that scans and shares in real time blood samples at high resolution.

By analyzing thousands of cells in minutes, the app revolutionized hematology and cell morphology, enabling the early detection of hematological-based diseases like cancers, infections or anemia. The early detection of these diseases improves the patient’s chances of recovery and improves the quality of their life.  

6. Remote patient care 

Remote patient care uses AI-powered technology to provide healthcare services and monitor patients remotely. Telemedicine is a form of remote patient care that enables patients to receive real-time medical treatment and consultations wherever they are located as opposed to seeing a doctor in-person. This ensures patients in even the most remote locations receive access to healthcare services and decreases healthcare expenditures by reducing hospital visits.  

Using AI algorithms, a wearable device worn by a diabetic patient can detect and transmit to patients and healthcare professionals abnormal readings of glucose levels. This triggers treatment plan adjustments remotely, helping keep the medical costs under control. However,  AI can do much more than monitoring glucose levels.  

VirtuSense, for example, uses AI to remotely identify patients’ “intent to exit their bed 31-65 seconds before they get up and sends alerts to the right staff immediately” contributing to a reduction in the number of falls.

7. Fraud detection 

The Centers for Medicare and Medicaid Services (CMS) is using AI and ML to “combat and prevent fraud, waste and abuse.”

Fraud is affecting healthcare systems at different levels and stakeholders have already begun to use AI algorithms as a tool to fight against it.  

Whether it is insurers getting billed for services not rendered, faulty test kits or devices or surgeons conducting unnecessary operations to obtain higher insurance payments, AI helps detect fraud by processing extensive medical and billing data in search of deviations and irregular patterns. AI can spot and duplicate billing, helping prevent fraud and ensuring patients benefit from appropriate care. 

AI technology can compare vast data from several sources to determine connections that might be overlooked by human checking. Moreover, machine learning algorithms adapt in time improving their ability to identity fraudulent claims. Such developments would prevent fraud, which translates into saving money that can be used as originally intended: such as providing high-quality care to patients. 

8. Accurate and early cancer diagnosis 

Cancer kills ten million people every year, being the leading cause of death worldwide. However, if detected and treated at an early stage, many cases of cancers can be healed/cured. Given that lung cancer is the biggest cause of cancer mortality worldwide, scientists and doctors have designed an AI tool that can accurately detect early-stage lung cancer to speed up diagnosis and set patients enroute for treatment. 

A team of experts from the Institute of Cancer Research, London, Royal Marsden NHS foundation and Imperial College London have used radiomics to identify if abnormal growths on CT scans are cancerous. Radiomics is a quantitative approach that uses advanced mathematical analysis to enhance the data available to clinicians. In this study, radiomics was used to extract essential information from medical images easy to miss by the human eye. 

The AI model identified with accuracy large cancerous lung nodules, a result that enables doctors to make quicker decisions about medium-risk patients who have abnormal growths on their CT scans. This enables early-stage diagnosis that increases the five-year survival rate compared to those whose cancer is detected at a later stage.

9. AI assisted gene editing in treatment design 

Diseases such as sickle cell anemia, cystic fibrosis and Tay-Sachs disease are caused by errors in the order of DNA letters that codify the operating instructions for every human cell. In some cases, these errors can be corrected with a gene-editing process that rearranges these letters.

Other diseases are caused by problems in how the cellular machinery reads DNA, a process known as epigenetics. Traditionally, a gene provides the recipe for a particular protein and joins molecules called transcription factors that instruct the cell how much of that particular protein to produce. When this process doesn’t go as planned, over or underactive genes lead to diseases like cancer, diabetes and neurologic disorders. This prompts scientists to search for solutions to restore the normal epigenetic activity.

Using AI tools, researchers have developed zinc-finger (ZF) editing, a technique that can change and control genes. Although the artificial zinc fingers are challenging to design for a specific task, according to one study published in January 2023, in the future, this technique may help correct diseases caused by multiple genetic factors, from autism to heart disease and obesity. 


With a projection of more than $187 billion by 2030 at global level, Artificial Intelligence in healthcare has become a constant of our lives and will continue to evolve. To explore its benefits, healthcare organizations and tech companies will need to work side-by-side to ensure that the technology is used in a responsible and ethical way. AI-driven solutions and tools can address many of the challenges faced by healthcare systems, from drug development and remote patient care to early detection of cancer and medical imaging. AI can contribute to lower costs, better quality of care and save more lives. 

About the Author

Dr. Liz Kwo is chief commercial officer of Everly Health and a serial healthcare entrepreneur, physician and Harvard Medical School faculty lecturer. She received an MD from Harvard Medical School, an MBA from Harvard Business School and an MPH from the Harvard T.H. Chan School of Public Health.


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