Artificial Intelligence (AI), with its wide range of applications, is enhancing the speed and accuracy of businesses across various industries, including manufacturing, healthcare, finance, marketing, and many others. To take advantage, globally, companies, whether big or small, are actively adopting artificial intelligence technology in their systems and processes. Apart from enhancing the capabilities, it is also significantly reducing the operational costs, improving their efficiency, and enhancing the customer experience, along with the revenue growth. 

Artificial Intelligence in the Healthcare Sector

Healthcare is a frontrunner in adopting new technologies & innovation in its processes. Similarly, in the case of Artificial Intelligence, there has been a significant advancement over the past few years. Pharmaceutical and biotechnological companies, MedTech, and startups are actively working to deliver innovative AI products in healthcare across various segments, including patient monitoring, drug discovery, preclinical experiments, clinical trials, genomics, and early diagnosis and detection AI tools in healthcare. AI-based robot-assisted surgeries, cybersecurity, patient management, and fraud detection are other areas in which AI is making significant progress.

Let’s explore some of the most innovative artificial intelligence applications in healthcare as we dive further into this blog and uncover the possibilities it brings to the industry.

Artificial Intelligence in Drug Development

Drug Discovery takes a lot of time, money, and effort. On average, the drug development process takes anywhere between 10 to 15 years & costs billions to the manufacturers. Similarly, the effort can’t be justified if only one drug gets approved for every 100 medications in the first stage of a clinical trial for a particular indication.

To overcome these challenges, many established companies and startups are investing heavily in artificial intelligence for the drug development process. Starting from screening chemical properties to find new drug targets and from molecular design to organizing drug databases, all these segments are slowly incorporating AI. The preclinical stages also use AI to select the most suitable animal model for a particular condition or disorder. It is expected that in some cases, if the AI is fully employed in the clinical trials, it can save up to 90% of the drug development cost. 

Clinical trials encompass a broad range of activities, one of which is the Clinical Trial Participant Identifier. The selection of accurate target patient populations, recruitment, and participant retention are the major components of the Clinical Trial Process and can provide authentic results. Using the conventional method to select the desired patients matching the desired phenotype can take too much time, which is also a labor-intensive process. However, with natural language processing (NLP), the issue can be resolved significantly.

Globally, some prominent pharmaceutical giants adopting Artificial Intelligence drug discovery solutions include EVERSANA, Lyfegen, Syneos Health, McKinsey & Company, ICON plc, Clarivate, Thermo Fisher Scientific, Viseven, ZS Associates, Cloud Pharmaceuticals, and many others. 

Discover how Artificial Intelligence is transforming drug commercialization—explore our expertly curated report for in-depth insights.

AI as a Virtual Nursing Assistant

After drug development and treatment, the Nursing Assistant is the second most vital application of artificial intelligence in healthcare. Globally, there is a vast demand for nursing professionals. With the help of AI, it is much easier for the healthcare organization to manage and engage with a high number of patients at a time. Not just in terms of volume, but also with the desired need. An AI-powered Virtual Nursing Assistant can monitor a patient’s condition and follow up after critical treatment. Additionally, Virtual Nursing Assistants can be used at various stages of treatment and in between doctor visits to provide wellness checks through voice and AI. 

Nuance is a Medical Record Navigation application that helps automate the process of documenting clinical information, demonstrating practical uses of AI in clinical workflows. Suki, formerly called Robin AI, is another Virtual Nursing Assistant that aids physicians in managing medical documentation, showcasing one of the key AI uses in administrative support. In the Medical Transcription segment, Robin Healthcare acts as a virtual scribe, documenting clinical information in real-time based on natural dialogue between a physician and patient, using machine learning, a prominent use of AI in healthcare that reduces clinician burnout and improves documentation accuracy.

Additionally, companies like MedWhat, MedRespond, Welltok, Kognito, and iDAvatars offer innovative Virtual Nursing Assistance solutions, further expanding the uses of AI in patient engagement, triage, and support. These technologies exemplify how the use of AI in healthcare is streamlining operations, enhancing clinical efficiency, and supporting better decision-making. Apart from these, several other companies are actively working in the market to address a wide range of healthcare needs through AI-driven innovations.

AI in Preliminary Diagnosis: An Intelligent Symptom Checker

It is observed that in the US, nearly 5% of outpatients receive an incorrect diagnosis, which in some cases can be fatal. To overcome the diagnostics challenges, AI and machine learning are providing great help to doctors & physicians. AI can mimic human-like cognitive functions and, in some cases, especially in diagnostics, can significantly improve the physicians’ judgment and can also help to make accurate decision-making.

AI and ML use advanced algorithms to detect patterns in medical records, CT scans, x-rays, and other healthcare data to provide more accurate diagnoses. Additionally, it can detect and eliminate anomalies, thus providing better outcomes for diagnoses. Apart from high accuracy, AI can reduce the overall time period between tests and treatments. Currently, AI-based software is being evaluated for the diagnosis of diseases such as skin cancer, leukemia, dementia, and other conditions.

But have you ever wondered why AI-driven diagnostics are set to revolutionize healthcare? Click here to explore our expertly curated blog and uncover the exciting potential of AI in transforming diagnostics and patient care.

Dosage Error Reduction 

Medical fallacies such as surgical errors, laboratory errors, medication errors, documentation/computer errors, and healthcare-associated infections are some of the causes of death that can be avoided to a large extent. The wrong medication or improper dosage can also be fatal in some cases. Currently, to overcome medical errors due to incorrect dosage, some studies are using AI to predict medication dosages, specifically in cases of chronic conditions where patients follow medication regimens for months or years. AI-powered dosing algorithms are expected to provide significant improvements in dose efficiency by reducing adverse drug events in certain critical diseases. Currently, Dosis, a San Francisco company, is offering an artificial intelligence-powered personalized dosing platform to manage chronic drug regimens. 

Personalized Healthcare Plans and Treatment With AI

Personalized medicine, also known as precision medicine, is a growing concept in healthcare. Each person responds differently to a similar disease and its symptoms. To provide optimum results, personalized medicine is intended to categorize patients into different groups based on medical decisions, practices, interventions, and products, as determined by the physician. The precision medicine approach leverages a patient’s genetic history, environmental factors, and lifestyle to suggest an optimum treatment plan.

The use of AI in the medical field is revolutionizing this area by enabling the classification of diseases through various algorithms, resulting in more accurate diagnoses, early disease detection, predictive analytics, and treatment optimization. A prominent example of AI in healthcare is the January 2022 collaboration between Health at Scale, a healthcare machine intelligence company, and Walmart. The partnership aimed to produce personalized provider recommendations for Walmart associates and their families using AI-powered insights.

With rapid advancements in machine learning, AI is expected to significantly enhance the accuracy and prediction of health outcomes for individual patients, driving the next era of patient-centric care.

Artificial Intelligence in Oncology

AI is revolutionizing cancer treatment by enhancing the detection, diagnosis, and treatment of cancer. With its ability to analyze the tumor microenvironment, AI makes it easier to recommend personalized treatment plans and understand how cancer cells become resistant to drugs. The AI-powered cancer diagnostics market, valued at USD 1 billion in 2024, is projected to grow at a CAGR of 11.16%, reaching USD 2.4 billion by 2032. This growth underscores AI’s impact on early detection and improving survival rates through personalized, data-driven treatment strategies.

Recent advancements in AI cancer diagnostics are making significant strides. In April 2025, Paige’s PanCancer Detect, an AI-powered tool, earned FDA Breakthrough Device designation, enabling pathologists to detect both common and rare cancers across multiple tissues and organs. This tool accelerates diagnosis and improves workflow efficiency. In February 2025, Ibex Medical’s Prostate Detect received FDA 510(k) clearance, enhancing diagnostic accuracy by helping pathologists identify missed or subtle prostate cancers from scanned biopsy images. Additionally, in January 2025, Tempus AI launched xT CDx, a 648-gene NGS panel, which helps with therapy selection and clinical trial enrollment.

These innovations highlight the growing role of AI in cancer care, making diagnostics faster, more accurate, and personalized for individual patients. As AI continues to evolve, it will reshape the future of oncology, improving patient outcomes and making personalized care more accessible.

Top pharmaceutical companies and healthcare organizations, including iCAD, Inc., ibex-ai, Roche Diagnostics, Kheiron Medical Technologies Limited, MVision AI Inc., Siemens Healthineers AG, GE HealthCare, NVIDIA Corporation, Digital Diagnostics Inc., IBM Corporation, Azra AI, ConcertAI, PathAI, Median Technologies, Paige AI Inc., Therapixel, Flatiron, Freenome Holdings Inc., Onc.AI, and Sonrai Analytics, have already collaborated with or integrated AI technologies in cancer diagnostics.

For an in-depth look at how AI is transforming cancer diagnostics, check out our full blog on Artificial Intelligence in Cancer Diagnostics: Revolutionizing Early Detection and Treatment.

AI in Gene Editing

CRISPR-Cas9 is an emerging technology in the healthcare domain and aims to revolutionize the segment with immense benefits. CRISPR-Cas9 can edit DNA sequences to correct gene defects and treat diseases. However, accuracy and safety are the two major concerns with gene editing, which can have dangerous consequences for an individual’s genome. 

Machine learning holds the potential to improve the outcome as it can analyze sequenced data at a much faster rate, and that too with high accuracy. It can also predict the genetic alterations associated with diseases and thereby reduce the risk of ineffective treatment and false diagnoses for patients with different genetic conditions. Globally, some of the genomics companies are already working to assess the potential of ML in disease risk assessment.

AI Robot-Assisted Surgery

Robotic surgery is set to take healthcare to the next level with its vast benefits and artificial intelligence applications in healthcare. The demand and potential in the surgical segment are quite vast. Artificial Intelligence is an essential part of robots. AI-based algorithms can provide greater precision and control for surgical robots, streamlining the interaction between them and surgeons. AI collects data during surgery, and over time, it can recognize and determine the movements and patterns of the surgeon. Additionally, in some areas, AI robots can have a huge demand, like hair transplant procedures. Surgical robots can extract hair follicles and implant them with greater precision and speed. Similarly, it has potential artificial intelligence applications in healthcare in the surgery of delicate organs, such as the brain and heart.  

AI can identify patterns within surgical procedures to improve best practices and enhance the control accuracy of surgical robots to submillimeter precision. AI is also being used with machine vision to analyze scans and detect cancerous cases. Laparoscopic video analysis of surgeries, such as sleeve gastrectomy procedures, helps identify missing or unexpected steps in real-time.

Rare diseases, also known as orphan diseases, are one of the major deadlocked segments in the healthcare domain, with limited research and development in terms of clinical research. Globally, there are over 7,000 rare diseases, and only 5% have a treatment on the market. These disorders exhibit a wide range of symptoms that can be difficult to diagnose and treat.

Artificial intelligence (AI) in the Rare Disease segment can immensely transform the diagnostics & treatment approaches. AI technologies can integrate and analyze structured and unstructured data from various sources, including devices, patient registries, digital platforms, and other medical records. Based on the data, AI can create multiple logical disease indicators for rare diseases that can be used by clinicians and researchers. If implemented well, AI can provide immense benefits to the people who have been suffering from rare diseases.

FindEHR is one of many artificial intelligence applications in healthcare that are being developed for rare diseases. Some other emerging AI applications for the rare disease space include Fabric GEM by Fabric Genomics, Face2Gene by FNDA, and Healnet by Healx, among others.

Fabric GEM uses AI to analyze potential gene variants and quickly identify the root cause of the disease, allowing patients to receive the right treatment sooner. Moreover, Face2Gene uses computer vision to analyze facial features and identify rare genetic disorders. Healnet uses AI drug discovery to predict which known drugs or drug combinations have the highest chance of success in treating rare diseases.

As people become more health-conscious, the demand for health monitoring and wearable devices is also growing tremendously. Wearables monitor physical parameters, such as strain, pressure, temperature, heart rate, and other bodily variations. Additionally, the AI-powered devices equipped with smart sensors can help in early diagnostics, remote patient monitoring, managing chronic conditions, and, in some cases, can assist in handling emergency health issues. Along with Artificial Intelligence, the incorporation of Big Data, the Internet of Things (IoT), and 5G is also anticipated to make health monitoring with wearables more efficient and flexible. Eko, Ava, Sumu Band, and Livio Edge AI are some of the key companies actively working in the AI-based wearables devices segment. Due to the high demand, more companies are likely to enter the segment with new and innovative technologies.

AI elements have already been used in cardiology for about a decade. Eko, a California-based startup known for its digital stethoscopes, introduced an AI-powered platform that includes advanced heart monitors, patient and provider software, and AI-powered reporting.

AI is playing a transformative role in advancing women’s health. Ava, a health tech company based in Zurich and San Francisco, has developed a wearable device that exemplifies the innovative uses of AI, including the analysis of physiological data to monitor multiple parameters and accurately predict the fertile window.

Several AI-powered wellness platforms, such as Prelude, Univfy, Progyny, Ovia Health, Kindbody, and Carrot Fertility, offer 24/7 support, personalized fertility insights, and expert consultations. These platforms help women make informed decisions about fertility, pregnancy, and family planning.

Notably, Kindbody is expanding into new markets and investing in AI-driven enhancements to patient care, while Carrot Fertility recently published a 2024 IVF outcomes study highlighting improved success rates and healthier singleton pregnancies.

These developments highlight the growing impact of AI in providing personalized, accessible, and effective reproductive healthcare.

AI Applications in Mental Health: Revolutionizing Diagnosis and Treatment

Artificial Intelligence is reshaping the landscape of mental healthcare, bringing forth innovative solutions that are transforming diagnosis, treatment, and ongoing care. AI-powered chatbots, virtual therapists, and predictive analytics are driving a shift from reactive to proactive mental health management, offering AI tools in healthcare like mood tracking apps and remote monitoring devices to ensure personalized and accessible care. Real-time crisis interventions and AI-driven support are enhancing the efficiency and accessibility of mental health services, with companies like Lyra Health, Woebot Labs, Talkspace, Spring Health, Wysa, Kintsugi Health, Mindstrong, and NeuroStar TMS Therapy leading the charge in providing scalable, tech-driven solutions that complement traditional human expertise.

Despite the promising progress, challenges such as ethical concerns around data privacy, algorithmic bias, and the irreplaceable role of human empathy remain at the forefront of AI integration in mental health. Nevertheless, the potential to break down barriers and offer timely interventions is undeniable. With AI’s involvement, mental health systems are evolving to provide smarter and more effective care, pushing boundaries in diagnosing and treating mental health disorders, and improving patient outcomes across the globe.

The mental health crisis is one of the most pressing global health issues, with one in eight people worldwide affected by psychiatric disorders. The rise in prevalence of conditions such as depression, anxiety, ADHD, PTSD, and other complex disorders is a direct reflection of increasing societal pressures, environmental stressors, and limited access to care. While mental health awareness has grown, gaps in diagnosis, treatment, and access continue to leave millions underserved. The approval of digital therapeutics like Rejoyn for major depressive disorder and EndeavorRx for ADHD shows how AI can revolutionize care by providing innovative, non-traditional treatments with proven efficacy.

AI has the potential to close these gaps by making mental health services more accessible, timely, and tailored to individual needs. With technologies such as NightWare, NeuroStar TMS Therapy, Wysa, and Prism for PTSD, AI is already making waves in improving treatment outcomes and advancing care options.

Want to know how AI is transforming mental health care? Click here to read our full blog and dive deeper into the innovations and future artificial intelligence applications!

The Growing Importance of AI Fraud Detection

According to various studies, in the US, nearly 22% of all claims are flagged as fraud, waste, and abuse (FWA), which costs insurance providers and other stakeholders significantly. As per the National Healthcare Anti-Fraud Association, payers spend up to $68 billion a year due to fraud in the United States. The Fraud can be from any of the parties involved, such as Insurance carriers, Insurance subscribers, or Service Providers.

In healthcare, data is collected across various points over a long period. Currently, fraudulent detection activities use intricate and complicated patterns. With the help of data mining and machine learning tools, the fraud detection activities can be automated, and that too at a much faster rate. Additionally, organizations can save millions of dollars related to waste, fraud, and abuse.

The future artificial intelligence applications for fraud detection and management will be beneficial in the long run, however, adoption rates will vary across industries, companies, and countries.

Cybersecurity With AI

As the dependence on data increases, protecting sensitive data and preventing it from ransomware attacks is also becoming more difficult. Globally, several sectors are struggling to manage data breaches as technology advances. Artificial Intelligence has emerged as a potential tool to provide viable solutions for data protection, and companies across different industries are actively investing in AI.

If the past trends are analyzed, the healthcare industry has observed the highest number of cyber attacks compared to other sectors. The attack is also rising at a phenomenal rate. Medical systems and patients’ health data are at greater risk. Organizations working in the healthcare industry are recognizing the power of AI in streamlining the process, ensuring compliance, and mitigating human error. AI-driven programs are capable of collecting valuable data to gain new insights into unfamiliar patterns, suspicious activity by cybercriminals, and loopholes in the system. The AI application is well capable of handling big, complex data problems and is also capable of responding to data breaches faster with greater precision. However, no technology can ever be a hundred percent secure; there are always some chances that hackers may somehow access AI as well. 

Some early AI adopters, such as Google, IBM/Watson, Juniper Networks, etc., have made their data secure and independent. Gmail has used machine learning techniques to filter emails since its launch 18 years ago. On the other hand, IBM has increasingly relied on its Watson cognitive learning platform for “knowledge consolidation” tasks and threat detection using machine learning. Cognitive computing with Watson for Cyber Security offers an advanced type of artificial intelligence, leveraging various forms, including machine learning algorithms and deep learning networks, that become stronger and smarter over time, and these approaches mirror the evolving types of artificial intelligence in healthcare aimed at enhancing clinical decision-making and patient outcomes.

Apart from the mentioned segment, there are many other areas where AI can be implemented. Early disease detection, reducing medication non-adherence issues, streamlining patient experience with real-time data, and virtual reality-enabled robotics surgery are some other potential artificial intelligence applications in healthcare. Additionally, some companies are exploring the possibility of using AI in neurological diseases and trauma. 

The Scope of Future Artificial Intelligence Applications in the Evolving Healthcare Market

Artificial Intelligence is still in the early stages of adoption within the healthcare sector, but its potential is rapidly gaining traction. Over the next few years, AI is expected to significantly expand its footprint across multiple domains, especially in clinical trials, diagnostics, drug development, and patient care, bringing transformative changes to healthcare delivery and improving lives globally.

In the clinical trials landscape, AI is driving innovation by enhancing trial design, optimizing site selection, accelerating patient recruitment, predicting outcomes, and enabling adaptive trial strategies. These advances are helping reduce trial costs, shorten development timelines, and improve overall success rates.

Several leading companies are pioneering the integration of AI in clinical trials and healthcare. These include TEMPUS, NetraMark, ConcertAI, AiCure, Medpace, Inc., ICON plc, Charles River Laboratories, Dassault Systèmes, Oracle, Certara, Cytel Inc., Phesi, DeepHealth, Unlearn.ai, Inc., H1, TrialX, Suvoda LLC, Risklick, Lokavant, and Research Solutions. Alongside them, major players like General Electric (GE), Koninklijke Philips, Intel Corporation, Fabric Genomics, Nuance Communications, NVIDIA, Shimadzu, Recursion Pharmaceuticals, Siemens Healthineers, CloudMedx, IBM Corporation, DeepMind Technologies Limited, and Bay Labs are also propelling AI-led innovation in clinical and diagnostic settings.

Together, these companies are shaping a smarter, data-driven clinical trial ecosystem poised to revolutionize how therapies are discovered, tested, and brought to market.

Unlock the Future of Clinical Research – Dive into our comprehensive report on Artificial Intelligence (AI) in Clinical Trials Market to explore key trends, innovations, leading players, and growth opportunities shaping the next generation of clinical development.

Currently, the North American region is dominating the Global Artificial Intelligence in the Healthcare Market and is likely to dominate the segment due to high purchasing power, well-established healthcare infrastructure, higher prevalence of cancer, rare, genetic diseases, technological literacy, developed IT infrastructure, and higher adoption of digital technologies in the region. The developing countries are facing challenges such as the rising prevalence of chronic diseases, changing lifestyles, and the growing geriatric population, among several others. To overcome these challenges and provide a viable solution, the developing economies, especially in the Asia Pacific region, are also looking forward to adopting AI in healthcare, which offers immense opportunities for companies to capture the untapped and growing market. Additionally, the European nations and the Gulf countries are also investing heavily in the Artificial Intelligence in healthcare market. 

As per DelveInsight, the rise in investments from private investors & venture capitalists, the rising prevalence of cancer and rare diseases, increasing R&D activities in the genomic & biomedical segment, rising awareness, and adoption of technology are expected to drive the growth of the healthcare AI applications market in the coming years. The opportunities for MedTech and health companies are vast in terms of market share and revenue. Similarly, the scope of mergers & acquisitions, collaborations, and partnerships across healthcare verticals is also high. The continuous support, favorable initiatives, expenditure, and funding opportunities from governments across the region to implement AI in healthcare are another major factor expected to contribute immensely to the AI market growth.