Digital: AI & ML

From accelerating drug discovery to enabling earlier cancer diagnosis, how is artificial intelligence redefining value creation, transforming patient care and prompting a shift from reactive treatment to proactive intervention?

Bhargava Reddy at Qure.ai

Artificial Intelligence (AI) has moved beyond being merely a promising technology; today, it is actively reshaping the pharma and healthcare landscape. It is becoming increasingly apparent that strategic deployment of AI serves as a catalyst for innovation, significantly enhances patient outcomes and redefines value creation across the entire healthcare continuum.

Traditionally, pharmaceutical innovation has depended on extensive, pipeline-heavy R&D models that can span decades and involve substantial uncertainty and expense. However, AI-driven transformation is ushering pharma into an era characterised by agility and data-centric innovation. According to a report by McKinsey & Company, AI adoption has notably reduced drug development timelines, shortening them from an average of 11.7 years to approximately 9.8 years.1 AI’s capabilities in accelerating compound screening and streamlining regulatory submissions empower pharmaceutical companies to realise greater value within these shorter product life cycles.

This shift goes deeper than merely speeding up processes; it fundamentally redefines pharma’s business model, shifting the focus from blockbuster drugs designed for mass markets toward precision-targeted therapies and AI-enabled diagnostics. In this evolving model, data has become as essential as the molecular compounds themselves, ushering in a new paradigm we might call ‘data as the new molecule’. Precision diagnostics driven by AI are essential for enabling earlier interventions and prolonging patient life cycles, significantly increasing potential return on investment, particularly within chronic disease management and oncology.

Earlier detection, better outcomes: AI’s clinical and commercial upside

Early detection exemplifies this transformation vividly. AI-powered diagnostics now allow healthcare providers to detect diseases such as lung cancer at significantly earlier stages, often before symptoms present. Early-stage detection

substantially increases patient eligibility for drug treatment rather than surgical interventions, improving clinical outcomes for patients and unlocking new opportunities for downstream revenue, particularly in high-value therapeutic areas like oral chemotherapy. The measurable impacts of AI on clinical decision-making further highlight its strategic importance. Increased diagnostic accuracy, optimised clinical workflows, and shorter time-to-treatment are among the notable benefits that have captured the attention and earned the confidence of hospital boards and executive leadership teams. For example, AI systems analysing medical imaging have demonstrated significant improvements in diagnostic accuracy, helping clinicians make more informed decisions swiftly.

AI in clinical trials: reinventing research and recruitment

Beyond diagnostics, AI is evolving clinical trial design and patient recruitment. Predictive analytics – leveraging specific data – enable pharmaceutical companies to identify optimal trial candidates with remarkable efficiency. Early suggestions are that AI-driven recruitment can reduce the trial enrolment period by up to 30%, significantly lowering costs and accelerating the path to market.1,2,3 Additionally, AI’s role in virtual or decentralised clinical trials (DCTs),

which gained prominence during the COVID-19 pandemic, continues to grow. DCTs reduce geographic barriers, enhance participant diversity and streamline data collection through remote monitoring tools, reflecting a paradigm shift in clinical research methodologies. AI’s precision can also significantly enhance tumour assessment processes within oncology trials. Traditional manual assessments of tumour responses are time-consuming and often subjective. AI algorithms now enable automated, consistent and precise measurement of tumour progression, dramatically accelerating trial outcomes and therapeutic evaluations. This precision allows clinicians to swiftly adapt treatment plans, potentially improving patient prognoses and trial effectiveness.

Recognising this immense potential, pharmaceutical companies are actively establishing partnerships with innovative AI innovators and healthcare platforms. By embedding AI across entire value chains, from clinical trial design and patient recruitment to real-world evidence gathering and post-market surveillance, such strategic alliances are critical, not only for immediate operational efficiencies but also for long-term value creation.

Building trust in AI: ethics and transparency

Widespread adoption and successful implementation of AI hinge critically on trust and transparency. Ethical concerns around data privacy, consent, algorithmic bias and decision transparency have raised valid apprehensions among clinicians, patients and regulators alike. A recent study in Nature underscores that AI’s effectiveness in clinical settings depends heavily on the accuracy of algorithms and effective collaboration with clinicians.4 Pharma companies are responding proactively, collaborating with regulators, bioethicists and civil society to develop robust ethical frameworks. Initiatives such as human-in-the-loop models, where clinical experts validate and oversee AI-generated recommendations, ensure patient safety and build clinician confidence. Establishing ethical guidelines and governance frameworks is not merely a compliance exercise but a strategic imperative, as trust remains the currency of adoption in healthcare.

Executive vision and the patient-centric future

To truly leverage AI’s transformative capabilities, visionary leadership from the top is imperative. Industry leaders must spearhead this transition, shifting their view of AI from merely a technological tool to a central strategic differentiator capable of driving sustainable growth and delivering long-term shareholder value. Strategic executive oversight ensures AI adoption aligns directly with core business objectives and patient-centric outcomes. Perhaps most compellingly, the broader patient impact underscores AI’s profound value.5 Shifting healthcare from late-stage treatments to early-stage interventions mean far more than merely improved survival rates; it fundamentally enhances patients’ quality of life.

AI-enabled early diagnosis significantly reduces the need for aggressive treatments, frequent hospital visits and prolonged therapies, profoundly enhancing patient experiences.

Consider a scenario where lung cancer is detected so early through AI-enabled screening that a simple chemo pill replaces months of invasive treatments. This scenario is increasingly becoming a reality, reshaping the landscape of patient care. Though currently less emphasised, regulatory momentum and investor confidence are steadily rising. Global health regulators are becoming increasingly receptive to real-world evidence and insights derived from AI, which is boosting investor interest and confidence in AI-pharma collaborations.6

As we stand on the threshold of this significant transformation, the message for pharma and healthcare leaders is unmistakably clear: embrace AI not simply as another technology, but as a strategic partner capable of driving innovation, improving patient outcomes and reshaping healthcare’s value proposition. The era of AI-driven healthcare is here, now is the moment to fully harness its potential.

References

1. Visit: mckinsey.com/industries/life-sciences/our-insights/unlocking-peak-operational-performance-in-clinicaldevelopment-with-artificial-intelligence
2. Visit: pmc.ncbi.nlm.nih.gov/articles/PMC11491624/
3. Visit: nature.com/articles/d41586-024-00753-x4.
4. Visit: nature.com/articles/s41746-024-01064-15.
5. Visit: jto.org/article/S1556-0864(25)00455-1/fulltext
6. Visit: ey.com/en_gl/insights/law/how-the-challenge-ofregulating-ai-in-healthcare-is-escalating