AI and healthcare in 2025: Bridging gaps, saving lives, and redefining wellness

The health industry is increasingly adopting AI led technologies for patient-centric care delivery. Going forward, the healthcare market is expected to witness an upward trend and grow at a double-digit rate. The policy landscape has changed significantly after the pandemic, and there is an emergence of large payers and increasing patient awareness. These trends create new imperatives for managing healthcare organizations and demand management skills with an understanding of patient behaviour.

Advancements in AI & Gen AI are focused on creating seemingly original content like text, images, music, videos, and even novel chemical structures. It has intrigued people as it opens new possibilities for creativity and innovation, generating outputs with an unprecedented level of realism and naturalness. The applications of AI span a wide spectrum including product design, marketing, customer service, programming, and digital art. Notably, Gen AI has democratized AI by facilitating human-AI collaboration using simple conversational language. Here are the applicability & adoption areas in Healthcare leveraging AI that will proliferate in 2025 :

Personalized Care And Guided Diagnosis

Due to data strewn across multiple sources and systems, physicians struggle to provide the most effective care and treatment to patients at the lowest cost. AI can be used to tap into patients’ medical and family history and lifestyle, among other factors, to summarize key data points and recommendations for follow-up that can be reviewed by a physician during a patient visit. For instance, the usage of AI to help in the diagnosis of sepsis, a life-threatening disease, is being studied. Similarly, the ability to enhance medical imaging data and support the earlier diagnosis of diseases can drive positive patient outcomes. The ability to synthesize medical history and act as a co-pilot helps free up resources and drive better patient outcomes in an acutely stressed healthcare system.

Health Care Management

Payers and providers need to draw data from multiple systems and sources, as well as input from socioeconomic sources (maybe geographic, too), in order to frame policies that enhance the health outcomes for the population at large. By enabling targeted campaigns and the identification of at-risk population sets, AI helps pave the way for more outreach. For instance, large language models (LLMs) can be leveraged to create personalized educational materials for patients, outlining their medical conditions and treatment options. LLMs can also help overcome the language barrier by translating medical information to enable outreach.

Drug Discovery

Unlike traditional methods, AI can help generate novel drug candidates based on researcher-provided criteria and constraints. By training on data related to known drugs' chemical properties, it can generate new candidates with similar properties but different structures, potentially resulting in safer and more effective drugs. It can also predict the efficacy and safety of new drug candidates by analysing large data on drug-target interactions. AI identifies patient subgroups more likely to respond to a drug by analysing clinical data patterns, helping personalize drug therapy and improve patient outcomes. This translates into a potential reduction in costs and timelines associated with the development of new drugs by decreasing the need for expensive and time-consuming experimental trials.

Operational Efficiencies

A study found that access to AI-based conversational assistants increases workers’ productivity by 14% on average, "as measured by issues resolved per hour." Healthcare providers can reduce the administrative burden by implementing AI for various use cases, including enhancing member communications through digital channels and acting as a physician scribe. Using AI systems, physicians can automate the extraction of medically relevant information from discussion recordings, summarize the interaction and integrate notes into EHR systems. This can result in improved physician productivity and increased accuracy of patient data. Providers can also lower costs and offer improved member experience because AI enables more personalized and proactive communications through virtual agents (chatbots).

AI use cases can impact capabilities across the payer value chain. As in the case of providers, communication through digital channels like chatbots can be greatly enhanced by tapping into the knowledge base of the payer, including policy documents.AI can also be used for the auto-generation of approval and denial letters—this would encompass supporting responses to prior authorization requests and claim requests to improve speed and effectiveness.

According to analysts, "venture capital firms have poured in 5 BN USD in AI solutions over the last three years, with AI-enabled drug discovery and AI software coding receiving the most funding." Integrating generative AI with your business strategy can enable your organization to scale further, work faster, reduce costs and integrate new business models. And there is increased investment and traction on AI-focused offerings by providers to get you started.

All said, AI landscape is not without risks, especially when applied to a regulated industry such as healthcare. Bias and discrimination, intellectual property and copyright infringements, as well as privacy and security, are some of the risks your organization should be prepared to mitigate through governance.


(Sameer Dhanrajani - CEO, AIQRATE & 3AI)