AI integration in healthcare – Choose the right partners and tools

Key highlights

  • AI is revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatments, streamlining medical delivery, and facilitating industry advancements in predictive analytics, significantly improving patient outcomes.
  • Partnerships are key to creating value, as is evident from examples of successful integration of AI by healthcare companies.
  • Selecting the right vendor is crucial in unlocking AI's vast potential in healthcare, particularly enabling small and medium healthcare companies to navigate implementation challenges.

AI in healthcare has come a long way – from being a diagnostic aid to powering decision-making across medical and health management processes and driving innovation. Statista estimates that AI integration in healthcare could contribute to a $188 billion market, globally, by 2030 – potentially saving over 250,000 lives each year, according to the Journal of mHealth .

The growing need to cut healthcare costs, address medical staff shortages, enhance patient care, and manage digital health records is propelling the integration of AI in healthcare. This requirement is further fueled by increasing hospitalizations and resulting health data due to the rising prevalence of chronic conditions and a growing elderly population.

According to Forrester's report on the key takeaways from Healthcare Information and Management Systems Society (HIMSS) 2023, making a meaningful impact in this transformed ecosystem requires Healthcare Organizations (HCOs) to prioritize partnerships for digital transformation and IT modernization.

For successful integration of AI in healthcare, choosing a partner with expertise in compliance, integration capabilities, and domain knowledge is crucial.

Unlocking the benefits of AI in healthcare:

A proficient AI vendor partner is essential for guiding HCOs in selecting the most appropriate tools to harness the diverse benefits of AI in healthcare. These advantages include connecting disparate healthcare data, offering early disease detection and diagnostic assistance supported by real-time accurate data, reducing the cost of care, and enhancing healthcare operational efficiency.

However, according to Forrester, for HCOs to fully capitalize on the benefits of digital transformation partnerships, close collaboration is essential, with parties serving as co-innovation partners. The emphasis should be on creating substantial value together to enhance collective knowledge, streamline operations, and optimize care delivery.

For instance, Zebra Medical Vision uses AI algorithms to help radiologists analyze medical imaging data from CT scans, X-rays, and mammograms to detect various diseases. Another example is Tempus, which has partnered with Intermountain Healthcare to advance precision oncology research, using AI to enhance treatment for cancer patients.

To maximize returns on their AI investments, HCOs need co-innovation partners that have the expertise to define an AI strategy, identify best-fit tools and frameworks, integrate the chosen tools into the HCO IT landscape, and provide support from use case identification to post implementation stages.

Current trends and the future of AI in healthcare

Emerging AI use cases in healthcare point to groundbreaking advancements and potential in areas of diagnostics, treatment personalization, operational efficiency, and patient outcomes. According to a recent survey by Morgan Stanley Research, 94% of healthcare companies incorporate AI/ML to varying degrees, with the industry's average budget allocation for these technologies expected to nearly double from 5.7% in 2022 to 10.5% in 2024.

Drug discovery and precision medicine

According to a Harvard study, AI is revolutionizing drug discovery by reducing the time and costs of bringing new drugs to market. Traditional methods often take years and cost billions, but AI can generate novel drug molecules from scratch and conduct high-fidelity molecular simulations, minimizing the need for physical testing. Biotech company, Insilico Medicine, has been in the news for receiving the FDA's first Orphan Drug Designation for an AI discovered and designed drug. Moreover, key technology players such as Google Cloud have introduced AI-powered solutions, such as the Target and Lead Identification Suite and Multiomics Suite, to fast-track precision medicine and drug discovery.


AI has the potential to boost healthcare interoperability by bringing data in line with industry standards from multiple sources. For example, it can facilitate data exchange among diverse Electronic Health Record (EHR) systems, fostering real-time, seamless patient data exchange and supporting continuity of care. AI can also be used to create interface standards and system specifications for an interoperability workflow. Additionally, it can enhance healthcare interoperability by assisting the transition from legacy systems through intelligent data mapping and transformation. For instance, according to EHR Intelligence, HCOs are now utilizing AI to streamline health information exchange, generating Consolidated Clinical Document Architecture (C-CDA) formatted documents—a format used for health information exchange in the US—from fax messages.

94% of healthcare companies incorporate AI/ML to varying degrees, with the industry's average budget allocation for these technologies expected to nearly double from 5.7% in 2022 to 10.5% in 2024.

Predictive analytics

HCOs harness vast amounts of data. Predictive analytics helps transform this data into actionable information enabling healthcare providers to make data-driven decisions, optimize patient care, and manage resources efficiently. Notably, according to this study on JAMA Network, during the COVID-19 pandemic, predictive analytics helped the US National COVID Cohort Collaborative (N3C) accurately identify high-risk patients early, significantly reducing mortality rates from 16.4% in March 2020 to 8.6% in September 2020. Furthermore, while traditional systems may lead to hospital overstays subsequently increasing costs and infection risks, predictive analytics can forecast and reduce readmissions. For example, leveraging artificial intelligence with clinician verification, Corewell Health's predictive analytics initiative in January 2021 prevented 200 readmissions, resulting in a $5 million cost savings, as reported by Health IT Analytics.

Remote monitoring

AI in Remote Patient Monitoring (RPM) allows real-time monitoring, enhancing chronic condition management and enabling patient self-care. For example, a person with heart disease can use an AI-powered remote monitoring device, which checks real-time metrics such as pulse, blood pressure, and respiratory rate. The AI algorithm detects irregular patterns in the patient’s vital signs, signaling the possibility of an impending heart failure episode. If such a scenario takes place, a healthcare professional receives an alert in real-time and informs a clinician. The clinician can then provide remote care to the patient, thus improving care outcomes and preventing hospital admission. In the coming years, the high utility of RPM in tackling epidemics and infectious illnesses is expected to drive significant growth opportunities for market players, according to MarketsandMarkets.

Generative AI

Generative AI (Gen AI) in healthcare holds significant potential in analyzing data, enhancing medical imaging, simulating medical scenarios, and predicting outcomes, which will contribute to personalized patient care and overall healthcare improvement. For example, the Mayo Clinic in Minnesota is testing a Google chatbot named Med-PaLM 2 trained on medical licensing exam questions. The chatbot generates responses to healthcare queries, summarizes clinical documents, and organizes data, as reported by the Wall Street Journal. According to McKinsey, as Gen AI matures, it could converge with technologies like virtual and augmented reality, transforming healthcare delivery. For instance, healthcare providers could create visual avatars for patient interactions, or physicians could assess and improve how they approach new patients based on their experience of working with similar patients who have had positive health outcomes.

Overcoming challenges: AI implementation in small and medium healthcare practices

According to a recent Health IT Analytics report, large HCOs are more likely to embrace numerous AI solutions. However, the cost of implementing AI in healthcare can be a hindrance for small and medium practices, potentially limiting its adoption. Additionally, research indicates that healthcare startups venturing into AI often undergo greater scrutiny from clinical stakeholders who question the credibility of these startups and their solutions.

Small and medium players can overcome challenges in AI implementation by collaborating with partners that focus on developing cost-effective, scalable solutions that ensure regulatory compliance, adopt a user-centered design, and prioritize data security and privacy.

The expanding role of AI in healthcare holds promise for purpose-driven solutions to meet various business needs, including tailored systems that ensure patient safety, accelerate, and personalize treatment and adherence to medical protocols.

However, as AI technology grows, HCOs are expected to face increased regulatory scrutiny. Balancing innovation and regulation with the right vendor and tools will be crucial for maximizing the potential of AI in healthcare.