Alan Karthikesalingam

Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and prior scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system’s design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include: (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling; (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. While general purpose, we focus the validation in three biomedical applications: drug repurposing, novel target discovery ¹, and explaining mechanisms of anti-microbial resistance ². Specifically, Co-Scientist helped identify new drug repurposing candidates and synergistic combination therapies for acute myeloid leukemia, which were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI empowered scientists.

Wearable devices generate continuous physiological and behavioral data, but converting these signals into clinically reviewable biomarker hypotheses remains labor-intensive. We introduce CoDaS, an AI co-data-scientist that integrates multi-agent hypothesis generation, deterministic statistical analysis, adversarial validation and literature-grounded interpretation under human oversight. Across three wearable cohorts comprising 9,279 participant-observations, CoDaS prioritized candidate associations for mental-health and metabolic endpoints after internal checks for replication, stability, robustness and leakage. The system identified related circadian-instability signals associated with depression, including sleep-duration variability in DWB (𝜌 = 0.252, 𝑝 < 0.001) and sleep-onset variability in GLOBEM (𝜌 = 0.126, 𝑝 < 0.001), and derived a wearable cardiovascular-fitness index associated with insulin resistance (steps/resting heart rate; 𝜌 = −0.374, 𝑝 < 0.001). Adding these features to demographic models produced modest gains (Δ𝑅2 = 0.040 for depression, 0.021 for insulin resistance). In a 12-clinician review totaling approximately 25 active hours, clinician validity judgments aligned with CoDaS confidence tiers (𝜌 = 0.67, 𝑝 = 0.005), whereas added clinical value and confidence to act were rated lower. CoDaS supports traceable, hypothesis-generating prioritization of wearable candidate biomarkers.

Although large language models have shown promise in diagnostic dialogue ¹ , their capabilities for effective management reasoning, including disease progression, therapeutic response and safe medication prescription, have remained underexplored. We have advanced the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) ^1–3 using a new large-language-model-based agentic system optimized for multivisit clinical management and dialogue. To ground the reasoning of AMIE in authoritative clinical knowledge, we leveraged the long-context capabilities of Gemini ⁴ , combining in-context retrieval with structured reasoning to align its output with up-to-date clinical practice guidelines and drug formularies. In a randomized, blinded virtual Objective Structured Clinical Examination study, AMIE was compared to 21 primary care physicians (PCPs) across 100 multivisit case scenarios designed to reflect the guidance of the UK National Institute for Health and Care Excellence and BMJ Best Practice guidelines. AMIE was non-inferior to PCPs in management reasoning, as assessed by specialists, and scored better both with respect to preciseness of treatment and investigation, and in terms of its alignment with and grounding in clinical guidelines. To benchmark medication reasoning, we developed RxQA, a multiple-choice question benchmark that was derived from two national drug formularies (from the USA and UK) and validated by board-certified pharmacists. Although AMIE and PCPs both benefited from the ability to access external drug information, AMIE outperformed PCPs on higher-difficulty questions. Although further research will be needed before real-world translation of AMIE, its strong performance across evaluations marks a significant step towards use of conversational artificial intelligence as a tool in disease management.

Scientific discovery is driven by scientists generating hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent artificial intelligence (AI) system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and previous scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system’s design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling, and (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. Although this is a general-purpose system, we focus the validation in three biomedical applications: drug repurposing; novel-target discovery ¹ ; and explaining mechanisms of antimicrobial resistance ² . Specifically, Co-Scientist helped to identify new drug-repurposing candidates and synergistic combination therapies for acute myeloid leukaemia that were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI-empowered scientists.

Real-world clinical practice is inherently multimodal, relying on the synthesis of patient history with visual information such as medical imagery and clinical documents. Although large language models (LLMs) have shown promise in diagnostic dialogue, their evaluation has been largely restricted to text-only interactions, failing to capture the complexity of modern remote care delivery. Here we introduce a multimodal extension of the Articulate Medical Intelligence Explorer (multimodal AMIE), capable of gathering, interpreting and reasoning about multimodal data within a diagnostic conversation. To achieve this, we developed a state-aware dialogue framework that dynamically guides history-taking based on diagnostic uncertainty and evolving patient states, emulating the structured reasoning of experienced clinicians. We evaluated this updated, state-aware version of multimodal AMIE against primary care physicians (PCPs) in a randomized, blinded exploratory study comprising 105 simulated telehealth consultations, which included dermatology photographs, electrocardiograms and clinical documents. As assessed by 18 specialist physicians, multimodal AMIE outperformed PCPs not only in diagnostic accuracy but also in conversation quality, including history-taking and empathy. Specifically, multimodal AMIE demonstrated superior performance on 29 of 32 evaluation axes, including seven of nine metrics that assess multimodal reasoning. These results validate the efficacy of state-aware reasoning in bridging the gap between text and visual information and demonstrate the potential for artificial intelligence (AI) systems to augment clinicians in complex, multimodal diagnostic settings.

The scarcity of subspecialist medical expertise poses a considerable challenge for healthcare delivery. This issue is particularly acute in cardiology, where timely, accurate management determines outcomes. We explored the potential of Articulate Medical Intelligence Explorer (AMIE), a large language model-based experimental medical artificial intelligence system, to augment clinical decision-making in this challenging context. We conducted a randomized controlled trial comparing large language model-assisted care with the usual care of complex patients suspected of having a genetic cardiomyopathy, and we curated a real-world dataset of complex cases from a subspecialist cardiology practice. Nine participating general cardiologists were provided with access to both clinical text reports and raw diagnostic data—including electrocardiograms, echocardiograms, cardiac magnetic resonance imaging scans and cardiopulmonary exercise testing—and were randomized to manage these cases, either with or without assistance from AMIE. We developed a ten-domain evaluation rubric used by three blinded subspecialists to evaluate the quality of triage, diagnosis and management. In our randomized controlled trial with retrospective patient data, subspecialists favored large language model-assisted responses overall, and for the management plan and diagnostic testing domains, with the remaining domains considered a tie. Overall, subspecialists preferred AMIE-assisted cardiology assessments 46.7% of the time, compared with 32.7% for cardiologists alone ( P = 0.02), with 20.6% rated as a tie. Subspecialists also quantified errors, extra and missing content, reasoning and potential bias. Cardiologists alone had more clinically significant errors (24.3% versus 13.1%, P = 0.033) and more missing content (37.4% versus 17.8%, P = 0.0021) than cardiologists assisted by AMIE. Lastly, cardiologists who used AMIE reported that AMIE helped their assessment more than half the time (57.0%) and saved time in 50.5% of cases.

The Standards for Reporting Diagnostic Accuracy (STARD) 2015 statement facilitates transparent and complete reporting of diagnostic test accuracy studies. However, there are unique considerations associated with artificial intelligence (AI)-centered diagnostic test studies. The STARD-AI statement, which was developed through a multistage, multistakeholder process, provides a minimum set of criteria that allows for comprehensive reporting of AI-centered diagnostic test accuracy studies. The process involved a literature review, a scoping survey of international experts, and a patient and public involvement and engagement initiative, culminating in a modified Delphi consensus process involving over 240 international stakeholders and a consensus meeting. The checklist was subsequently finalized by the Steering Committee and includes 18 new or modified items in addition to the STARD 2015 checklist items. Authors are encouraged to provide descriptions of dataset practices, the AI index test and how it was evaluated, as well as considerations of algorithmic bias and fairness. The STARD-AI statement supports comprehensive and transparent reporting in all AI-centered diagnostic accuracy studies, and it can help key stakeholders to evaluate the biases, applicability and generalizability of study findings.